U.S. patent application number 10/638806 was filed with the patent office on 2004-05-20 for image analysis method, apparatus and program.
Invention is credited to Hori, Osamu, Ida, Takashi, Matsumoto, Nobuyuki, Takeshima, Hidenori.
Application Number | 20040096085 10/638806 |
Document ID | / |
Family ID | 32301803 |
Filed Date | 2004-05-20 |
United States Patent
Application |
20040096085 |
Kind Code |
A1 |
Matsumoto, Nobuyuki ; et
al. |
May 20, 2004 |
Image analysis method, apparatus and program
Abstract
An image analysis method for analyzing the motion of an object
by using the time-series image data. The method comprises inputting
time-series image data, generating time-series object image data by
extracting an object region from the time-series image data, and
analyzing the motion of the object from the time-series object
image data.
Inventors: |
Matsumoto, Nobuyuki;
(Kawasaki-shi, JP) ; Hori, Osamu; (Yokohama-shi,
JP) ; Ida, Takashi; (Kawasaki-shi, JP) ;
Takeshima, Hidenori; (Ebina-shi, JP) |
Correspondence
Address: |
OBLON, SPIVAK, MCCLELLAND, MAIER & NEUSTADT, P.C.
1940 DUKE STREET
ALEXANDRIA
VA
22314
US
|
Family ID: |
32301803 |
Appl. No.: |
10/638806 |
Filed: |
August 12, 2003 |
Current U.S.
Class: |
382/107 ;
382/294 |
Current CPC
Class: |
G06T 7/20 20130101; A63B
2220/806 20130101; G06T 2207/30241 20130101; A63B 69/3614 20130101;
G06T 2207/30221 20130101; G09B 19/0038 20130101; A63B 24/0003
20130101; A63B 69/36 20130101 |
Class at
Publication: |
382/107 ;
382/294 |
International
Class: |
G06K 009/00; G06K
009/32 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 26, 2002 |
JP |
2002-281206 |
May 14, 2003 |
JP |
2003-136385 |
Claims
What is claimed is:
1. An image analysis method comprising: inputting time-series image
data; generating time-series object image data by extracting an
object region from the time-series image data; and analyzing motion
of an object from the time-series object image data.
2. The method according to claim 1, further comprising:
synthesizing items of the time-series object image data into
synthesis image data; and displaying the synthesis image data.
3. The method according to claim 1, wherein the analyzing
comprises: computing a reference position for the motion of the
object from the time-series object image data; and synthesizing an
additional line indicative of the reference position with the
object image data.
4. The method according to claim 1, wherein the analyzing
comprises: computing a position of a center of gravity in the
object region from the time-series object image data; and
synthesizing an additional line indicative of the position of the
center of gravity with the object image data.
5. The method according to claim 1, wherein: the inputting includes
inputting object image data including first time-series object
image data and second time-series object image data; the analyzing
comprises: adjusting at least one of the first time-series image
data and the second time-series object image data; synthesizing
items of the first time-series object image data into first
synthesis image data, and items of the second time-series object
image data into second synthesis image data; and displaying the
first synthesis image data and the second synthesis image data in
parallel.
6. The method according to claim 1, wherein the analyzing
comprises: tracing a particular region in the object region from
the time-series object image data; and synthesizing a line
indicative of a trajectory of the particular region with the
time-series object image data.
7. The method according to claim 1, wherein the analyzing
comprises: computing a movement distance of the object region from
the time-series object image data; and computing a movement speed
of the object region from the movement distance of the object
region.
8. The method according to claim 1, wherein the analyzing
comprises: generating a trajectory of the object region from the
time-series object image data; and computing a curvature of a
portion of the trajectory.
9. The method according to claim 1, wherein the analyzing
comprises: generating a trajectory of the object region from the
time-series object image data; computing a first curvature of a
first portion of the trajectory, and a second curvature of a second
portion of the trajectory; and computing a ratio between the first
curvature and the second curvature.
10. The method according to claim 1, wherein the analyzing
comprises: generating a trajectory of the object region from the
time-series object image data; and computing an area of a region
defined by the trajectory of the object region.
11. The method according to claim 1, wherein the generating
comprises: extracting an object region from the time-series object
image data; allowing a position in the object region to be
designated; and setting a position of a particular point in
accordance with the designated position.
12. The method according to claim 1, further comprising
synthesizing results of the motion analysis with the time-series
image data and displaying a synthesis result.
13. The method according to claim 12, wherein the results of the
motion analysis are graphed and displayed.
14. The method according to claim 12, wherein the results of the
motion analysis are digitized and displayed.
15. An image analysis method comprising: inputting time-series
image data; tracing a particular region based on the time-series
image data; generating a trajectory of the particular region; and
computing a curvature of a portion of the trajectory.
16. An image analysis method comprising: inputting time-series
image data; tracing a particular region based on the time-series
image data; generating a trajectory of the particular region; and
computing an area of a region defined by the trajectory of the
particular region.
17. An image analysis apparatus comprising: means for inputting
time-series image data; means for extracting an object region from
the time-series image data input by the time-series image data
input means, to generate time-series object image data; and means
for analyzing motion of an object from the time-series object image
data extracted by the means for extracting the object.
18. An image analysis apparatus comprising: a processor; a memory
accessible by the processor; and a program stored in the memory,
wherein the program comprises: program code for inputting
time-series image data; program code for extracting an object
region from the time-series image data to generate time-series
object image data; and program code for analyzing motion of an
object based on the time-series object image data.
19. An image analysis apparatus comprising: a camera; a processor;
a memory accessible by the processor; a display; and a program
stored in the memory, wherein the program comprises: program code
for inputting time-series image data obtained by the camera;
program code for extracting an object region from the time-series
image data to generate time-series object image data; program code
for analyzing motion of an object based on the time-series object
image data; and program code for displaying an analysis result on
the display.
20. A program stored in a computer readable medium, comprising:
program code for inputting time-series image data; program code for
extracting an object region from the time-series image data to
generate time-series object image data; and program code for
analyzing motion of an object based on the time-series object image
data.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is based upon and claims the benefit of
priority from the prior Japanese Patent Applications Nos.
2002-281206 and 2003-136385, filed Sep. 26, 2002 and May 14, 2003,
respectively, the entire contents of which are incorporated herein
by reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to an image analysis method,
apparatus and program for analyzing the movement of an object from
images thereof contained in time-series image data.
[0004] 2. Description of the Related Art
[0005] In general, a technique for focusing on a certain object in
an image and analyzing the movement of the object is an important
technique concerning images. This technique is utilized to provide
TV viewers with effective synthesized images during a sports
program, or to analyze the movement of players in a tennis or golf
school for quantitative instruction, or to enhance the techniques
of athletes. "Multi-motion--Gymnast Form/Trajectory Display System"
(Image Laboratory, vol. 9, no. 4, pp. 55-60, published by Nippon
Kogyo Publisher on Apr. 1998) is a conventional example of a
technique for analyzing the movement of an object from images
thereof contained in time-series image data.
[0006] In this technique, firstly, only the region (object region)
including a target object is extracted from time-series image data.
The object region is extracted by, for example, computing
differences between frames and digitizing a region of a large
difference. Further, the object region can be extracted by a
chroma-key method for pre-photographing a moving object using a
one-color background, and extracting the object region utilizing a
difference in color, or a manual method for inputting the outline
of an object extracted by a user's operation with a mouse or
graphic tablet, or a method for automatically correcting the rough
outline of an object input by a user, as disclosed in Jpn. Pat.
Appln. KOKAI Publication No. 2001-14477.
[0007] As described above, only an object is extracted from
time-series image data, to thereby create object image data
comprising time-series images of an object region.
[0008] After that, the time-series object image data items are
superposed and synthesized in order, thereby creating a time-series
image synthesized in a stroboscopic manner.
[0009] Thus, the trajectory of an object can be grasped from the
display of synthesized time-series object image data. In other
words, the movement of the object can be visually analyzed.
[0010] However, the image analysis method of displaying extracted
time-series object image data items superposed on each other only
enables viewers to see the movement of an object as successive
images. This analysis is just a qualitative analysis as to how the
position of the object changes, but not a quantitative analysis as
to the degree of change in the position of the object.
BRIEF SUMMARY OF THE INVENTION
[0011] An advantage of an aspect of the present invention is to
provide an image analysis method, apparatus and program capable of
analyzing the movement of an object in a quantitative manner from
images thereof contained in time-series image data.
[0012] According to an aspect of the preset invention, there is
provided an image analysis method comprising: inputting time-series
image data; generating time-series object image data by extracting
an object region from the time-series image data; and analyzing
motion of an object from the time-series object image data.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING
[0013] FIG. 1 is a block diagram illustrating an image analysis
apparatus according to an embodiment of the invention;
[0014] FIG. 2 is a flowchart useful in explaining an image analysis
method according to a first embodiment;
[0015] FIGS. 3A, 3B, 3C, 3D, 3E and 3F are views illustrating
examples of time-series data items;
[0016] FIGS. 4A, 4B, 4C, 4D, 4E and 4F are views illustrating other
examples of time-series data items;
[0017] FIG. 5 is a view illustrating an example of an image
obtained by superposing, upon an initial frame of time-series image
data, object image data extracted from each item of the image
data;
[0018] FIG. 6 is a flowchart useful in explaining a motion analysis
step employed in the first embodiment;
[0019] FIGS. 7A and 7B are views illustrating a state in which
additional lines (swing axes) are superposed in the motion analysis
step of the first embodiment;
[0020] FIGS. 8A and 8B are views illustrating a state in which
additional lines (upper ends of objects) are superposed in the
motion analysis step of the first embodiment;
[0021] FIG. 9 is a view illustrating a state in which additional
lines (lattice lines) are superposed in the motion analysis step of
the first embodiment;
[0022] FIG. 10 is a view illustrating a state in which additional
lines (lattice lines) are superposed in a background in the motion
analysis step of the first embodiment;
[0023] FIG. 11 is a flowchart useful in explaining a motion
analysis step employed in a second embodiment;
[0024] FIGS. 12A, 12B, 12C, 12D, 12E and 12F are views illustrating
time-series object image data items obtained in the motion analysis
step of the second embodiment, each object image data item
containing a point indicative of the center of gravity;
[0025] FIGS. 13A and 13B are views useful in explaining a motion
analysis performed in the motion analysis step of the second
embodiment, utilizing changes in the center of gravity;
[0026] FIGS. 14A and 14B are views useful in explaining a
vertical-motion analysis performed in the motion analysis step of
the second embodiment, utilizing changes in the center of
gravity;
[0027] FIGS. 15A and 15B are views useful in explaining a
horizontal-motion analysis performed in the motion analysis step of
the second embodiment, utilizing changes in the center of
gravity;
[0028] FIG. 16 is a flowchart useful in explaining a motion
analysis step employed in a third embodiment;
[0029] FIGS. 17A and 17B are views illustrating examples of first
and second time-series object image data input in an object image
data adjusting step employed in the third embodiment;
[0030] FIGS. 18A and 18B are views illustrating examples of first
and second time-series object image data output in the object image
data adjusting step employed in the third embodiment;
[0031] FIG. 19 is a view illustrating comparison synthesis display
data output in a comparison synthesis step employed in the third
embodiment;
[0032] FIG. 20 is a flowchart useful in explaining a motion
analysis step employed in a fourth embodiment;
[0033] FIG. 21 is a view illustrating, using circular marks and
lines, the trajectory of a particular point in the fourth
embodiment;
[0034] FIG. 22 is a flowchart useful in explaining a motion
analysis step employed in a fifth embodiment;
[0035] FIG. 23 is a view useful in explaining a head-speed
measuring method employed in the fifth embodiment;
[0036] FIG. 24 is a flowchart useful in explaining another motion
analysis step employed in a fifth embodiment;
[0037] FIG. 25 is a view useful in explaining a method for
measuring the curvature of a swing arc in the fifth embodiment;
[0038] FIG. 26 is a flowchart useful in explaining a motion
analysis step employed in a sixth embodiment;
[0039] FIG. 27 is a view useful in explaining a method for
measuring the area and inclination of a swing plane in the sixth
embodiment;
[0040] FIG. 28 is a view useful in explaining a particular point
position setting step in a seventh embodiment;
[0041] FIG. 29 is a flowchart useful in explaining an image
analysis method according to an eighth embodiment;
[0042] FIG. 30 is a view illustrating changes in the center of
gravity in the eighth embodiment, utilizing a numerical value and a
graph;
[0043] FIG. 31 is a view illustrating the area of a swing plane in
the eighth embodiment, utilizing a numerical value; and
[0044] FIGS. 32A and 32B are views useful in explaining changes in
the position of a particular point on a trajectory.
DETAILED DESCRIPTION OF THE INVENTION
[0045] Embodiments of the invention will be described in detail
with reference to the accompanying drawings. FIG. 1 is a block
diagram illustrating the configuration of an image analysis
apparatus according to the invention. The image analysis apparatus
of the invention is formed of, for example, a computer that
executes an image analysis program recorded in a recording medium,
such as a semiconductor memory, CD-ROM, DVD, magnetic disk, etc.,
and is controlled by the executed program. This computer contains a
processor, a memory accessed by the processor, and an image
analysis program stored in the memory.
[0046] As seen from FIG. 1, the image analysis apparatus of the
invention comprises an image analysis control section 10, input
unit 12, display unit 14, tablet 15 and storage 16.
[0047] The image analysis control section 10 has a function for
receiving time-series image data from the input unit 12 or storage
16, and quantitatively analyzing the motion of an object from
images thereof contained in the time-series image data. This
function includes a function for measuring a change in the movement
of an object, the speed of a particular point in the object, the
trajectory of the particular point, the angle (curvature) or region
defined in an image when the particular point moves, and also a
function for digitizing or graphing them to analyze them.
[0048] The image analysis control section 10 is a function realized
by executing, using the processor, the image analysis program
stored in the memory. The image analysis control section 10
comprises function sections, such as a time-series image data input
section 20, object region extracting section 22, motion analysis
section 24 (object image data adjusting section 26, comparison
analysis section 28), analysis result display section 30, etc. Each
function section executes the following processing.
[0049] The time-series image data input section 20 receives
time-series image data as an analysis target from the input unit 12
(image pickup means) or storage 16. The time-series image data
input section 20 measures the position of a particular point of an
object while attaching characterizing color data to data
corresponding to the particular point, or picking up image data of
the object (seventh embodiment).
[0050] The object region extracting section 22 extracts an object
region as a motion analysis target from the time-series image data
input by the time-series image data input section 20, thereby
generating object image data.
[0051] The motion analysis section 24 performs a motion analysis of
an object on the basis of information concerning the shape of the
object region contained in the time-series object image data
generated by the object region extracting section 22. In the motion
analysis, the motion analysis section 24 performs the following
processing--superposition and synthesis of background image data
formed of additional line data and the time-series object image
data, detection of the position of the center of gravity in an
object region contained in the time-series object image data,
superposition and synthesis of two time-series object image data
items (first and second object image data items), superposition and
synthesis of the line indicative of the trajectory of the object
region (particular point) of the object image data, and the
time-series object image data, detection of the movement speed of
the particular point, computation of the curvature of the
trajectory of the particular point, measurement of the closed
region defined by the trajectory of the particular point,
computation of the inclination of the trajectory of the particular
point, etc.
[0052] The motion analysis section 24 comprises an object image
data adjusting section 26 and comparison analysis section 28. The
object image data adjusting section 26 performs deformation
processing on the two time-series object image data items (first
and second object image data items) extracted by the object region
extracting section 22. The comparison analysis section 28
superposes and synthesizes the two time-series object image data
items (first and second object image data items).
[0053] The analysis result display section 30 displays the analysis
result of the motion analysis section 24. For example, the analysis
result display section 30 synthesizes and displays time-series
object image data, and displays the analysis result using a
numerical value or graph.
[0054] The input unit 12 comprises a keyboard for inputting, for
example, user's control instructions to the image analysis
apparatus, a pointing unit, such as a mouse, and image pickup
means, such as a video camera, for inputting time-series image data
to be analyzed.
[0055] The display unit 14 displays, for example, the analysis
result of the image analysis control section 10, object image data
synthesized by the comparison synthesis section 28, and an analysis
result digitized or graphed by the analysis result display section
30.
[0056] The tablet 15 is used by a user to perform an input
operation for analyzing an object included in an image in the image
analysis control section 10, and allows a pen to point the position
of the object in the image. The tablet 15 is superposed on the
display surface of the display unit 14, and hence the user can
directly designate a position on an image that is displayed on the
display unit 14.
[0057] The storage 16 stores time-series image data 18 to be
analyzed, as well as various programs and data items. The
time-series image data 18 includes first and second time-series
image data items corresponding to different objects.
[0058] The embodiments of the invention will now be described.
[0059] (First Embodiment)
[0060] FIG. 2 is a flowchart useful in explaining an image analysis
method according to a first embodiment. In FIG. 2, step A1 is a
time-series image data input step for inputting time-series image
data. Step A2 is an object region extracting step for extracting an
object region from time-series image data to thereby generate
time-series object image data. Step A3 is a motion analysis step
for analyzing the motion of an object on the basis of shape
information contained in the generated time-series object image
data. Step A4 is an analysis result display step for displaying an
analysis result obtained at the motion analysis step.
[0061] Firstly, at the step Al at which time-series image data is
input by the time-series image data input section 20, time-series
image data obtained by, for example, the input unit 12 (image
pickup means such as a video camera), or time-series image data 18
prestored in the storage 16 is input. At this step, the time-series
image data as shown in FIGS. 3A-3F is input. The time-series image
data of FIGS. 3A-3F is obtained by photographing a player swinging
a golf club. Assume that the player and golf club are
to-be-analyzed objects in the time-series image data.
[0062] Subsequently, at the step A2 at which an object region is
extracted by the object region extracting section 22, it is
extracted from the time-series image data shown in FIGS. 3A-3F,
thereby generating time-series object image data shown in FIGS.
4A-4F. Time-series object image data is obtained by dividing, for
example, the time-series image data shown in FIG. 3A into a
background portion and object region portion (the player and golf
club), and extracting the object region portion. The object image
data contains color information and shape information concerning an
object image. In the time-series object image data shown in FIGS.
4A-4F, assume that the player portion is partitioned from the golf
club portion. The time-series object image data of FIGS. 4A-4F is
generated by daubing the portion other than the object region
portion on the basis of the shape information.
[0063] FIG. 5 is a view illustrating an example of an image
obtained by superposing, upon an initial frame of time-series image
data, object image data extracted from each item of the image data
(the time-series image data in this case does not correspond to
that shown in FIGS. 4A-4F). In FIGS. 3 and 4, although the
time-series image data (object image data) is thinned out for
facilitating the description, a larger number of time-series image
data items (object image data items) are actually used, as is
understood from FIG. 5 in which a larger number of time-series
image data items are superposed. The number of frames of
time-series image data per unit time can be determined on the basis
of the contents of image analysis, described later. Further, at the
analysis result display step, described later, a plurality of
object image data items are displayed superposed on each other. In
this step, however, the object image data may be thinned out to
make it easy to see the displayed image. Further, for facilitating
the following description, the drawings in which time-series image
data (object image data) is thinned out will be referred to.
[0064] As a method for extracting an object region, the following
may be utilized: a method for computing differences between frames
and digitizing a region of a large difference to extract an object
region; a chroma-key method for pre-photographing a moving object
using a one-color background, and extracting the object region
utilizing a difference in color; a manual method for inputting the
outline of an object extracted by a user's operation with a mouse
or graphic tablet; or a method for automatically correcting the
rough outline of an object input by a user, as disclosed in Jpn.
Pat. Appln. KOKAI Publication No. 2001-14477. At the object region
extracting step (step A2), the object region is extracted from
image data utilizing a certain method.
[0065] After that, at the step A3 at which a motion analysis is
performed by the motion analysis section 24, the motion of the
object is analyzed from shape information contained in the
extracted time-series object image data.
[0066] FIG. 6 is a flowchart useful in explaining the motion
analysis step employed in the first embodiment.
[0067] In an object motion analysis, in most cases, if the
trajectory of motion is observed while it is compared with a
certain reference line, the characteristics of the motion can be
clearly seen.
[0068] In the case of, for example, golf swing, it is considered
desirable to twist the body about the backbone when swinging a golf
club. Therefore, it is important for the analysis of a swing
whether or not the swing axis is kept constant during the swing
motion.
[0069] Firstly, the motion analysis section 24 computes, in a
reference computing step, the position of a reference line for the
motion on the basis of object image data generated at the object
region extracting step (step A3a1). In the case of image data
corresponding to a golf swing, the swing axis of an object (player)
is detected from shape information contained in, for example, the
object image data as shown in FIG. 4A. Assume that the swing axis
of the object corresponds to a vertical line that passes through
the position of the center of gravity in an object region contained
in the object image data shown in, for example, FIG. 4A (the
initial frame). The motion analysis section 24 computes the
position of the center of gravity in the object region from shape
information (concerning a player) contained in the object image
data, thereby obtaining the vertical line passing through the
position of the center of gravity.
[0070] Subsequently, the motion analysis section 24 synthesizes, in
a reference-additional-line synthesis step, superposes and
synthesizes the object image data and an additional line indicative
of the swing axis utilizing the position computed at the reference
computing step (step A3a2).
[0071] FIG. 7A illustrates an image obtained by synthesizing the
initial frame of the object image data and an additional line 50 so
that the line vertically passes through the position of the center
of gravity in the object region.
[0072] The position of the additional line 50 may be determined not
only from the object region in the initial frame, but also
utilizing any one of the frames ranging from the initial one to
final one. For example, the frame in which the head of the club
hits a ball, or a frame near this frame is detected, and the
additional line is determined on the basis of this frame. The frame
near the hit time can be detected by detecting, for example, object
image data indicating that the head of the golf club is lowest.
Further, the club head can be detected if it is designated as a
particular point, as in a fourth embodiment described later.
[0073] The analysis result display section 30 sequentially
superposes and synthesizes time-series object image data items,
each of which is obtained by synthesizing object image data and the
additional line 50 in the reference additional line synthesis step
using the motion analysis section 24. The display section 30
displays the synthesized object image data on the display unit 14.
Thus, the image as shown in FIG. 7B, obtained by synthesizing a
plurality of time-series object image data items and the additional
line 50 to be referred to when a movement of an object is
determined, is displayed on the display unit 14.
[0074] Various additional lines other than the additional line 50
indicative of the swing axis shown in FIGS. 7A and 7B can be
synthesized with object image data for assisting the motion
analysis of an object.
[0075] For example, the upper end of the object (the top of the
head) is detected from the shape information (concerning the
player) contained in the object image data shown in FIG. 4A,
thereby displaying, as an additional line 52, the horizontal line
touching the top of the head, as shown in FIG. 8A. With reference
to the additional line 52, the vertical movement of the head during
a swing can be determined.
[0076] The upper end of the object (the tope of the head) can be
detected, for example, in the following manner. The motion analysis
section 24 horizontally scans, for example, the initial frame of
object image data, beginning from the uppermost portion of the
frame, thereby detecting an object image. When the object image is
detected, it is determined whether the object image is the head or
part of the golf club. The object image corresponding to the golf
club (club head or shaft) is much narrower than the object image
corresponding to the head, from which whether the object image is
the head or part of the golf club is determined. For example, when
the object image data shown in FIG. 4A is horizontally scanned
beginning from the uppermost portion, the object image
corresponding to the golf club is detected before the image
corresponding to the head. In this case, when an object image other
than that corresponding to the golf club is detected, it can be
determined to correspond to the head.
[0077] Further, concerning the golf club, its color may be
prestored so that it can be determined from the color of a detected
object image, whether or not the object image corresponds to the
golf club, as will be described in detail in the fourth
embodiment.
[0078] Furthermore, when the horizontal additional line 52 is
displayed, the position of the additional line 52 may be determined
not only from the object region in the initial frame, but also
utilizing any one of the frames ranging from the initial one to
final one.
[0079] As a result, as shown in FIG. 8B, an image obtained by
synthesizing a plurality of time-series object image data items and
the horizontal additional line 52 to be referred to when a movement
of an object is determined, is displayed on the display unit
14.
[0080] Also, additional lines 54 arranged in the form of a lattice
and time-series object image data may be superposed and synthesized
for analyzing the motion of an object, as shown in FIG. 9 or 10 (in
the case of FIG. 10, the additional lines 54 and time-series object
image data are superposed on the first frame of time-series image
data that contains background data).
[0081] The position of one of the vertical lines included in the
additional lines 54 shown in FIG. 9 or 10 may be identical to that
of the additional line 50 shown in FIG. 7A or 7B. Similarly, the
position of one of the horizontal lines included in the additional
lines 54 may be identical to that of the line 52 shown in FIG. 8A
or 8B.
[0082] In addition, in FIGS. 7, 8, 9 and 10, the additional line(s)
50, 52, 54 is superposed on an image obtained by sequentially
superposing time-series object image data items. However, an image
obtained by sequentially superposing time-series object image data
items may be superposed on the additional line(s) 50, 52, 54.
[0083] As described above, in the first embodiment, synthesis of an
additional line and time-series object image data enables the
motion of an object to be quantitatively analyzed from images of
the object contained in time-series image data. In other words,
with reference to the additional line(s) 50, 52, 54, a movement of
a player who is swinging a golf club can be measured.
[0084] (Second Embodiment)
[0085] In a second embodiment, the position of the center of
gravity in an object region extracted as time-series object image
data is detected and used for the motion analysis of the object in
a motion analysis step similar to that of the first embodiment.
[0086] FIG. 11 is a flowchart useful in explaining the motion
analysis step employed in the second embodiment.
[0087] In a center-of-gravity computing step, the motion analysis
section 24 computes the position of the center of gravity in an
object region from time-series object image data that is extracted
from time-series image data by the object region extracting section
22 (step A3b1), thereby superposing the point (center-of-gravity
point) indicative of the position of the center of gravity
contained in object image data to obtain synthesis image data (step
A3b2).
[0088] FIGS. 12A-12F are views illustrating time-series object
image data items obtained in the motion analysis step of the second
embodiment, each object image data item containing a point
indicative of the center of gravity. The analysis result display
section 30 displays, on the display unit 14, the position of the
center of gravity in an object region included in each frame of
time-series object image data, the position of the center of
gravity being set as a center-of-gravity point 60 as shown in FIG.
13A. Alternatively, the analysis result display section 30
sequentially superposes and synthesizes time-series object image
data items, and displays a center-of-gravity-point train 62 as
shown in FIG. 13B. As a result, changes in the center of gravity
with time can be visually confirmed.
[0089] Further, to enable vertical changes in the center of gravity
to be confirmed, object image data can be displayed together with a
horizontal additional line that passes through the position of the
center of gravity in the object region of the object image data.
FIGS. 14A (14B) shows the case where a horizontal line 64
(horizontal lines 65) that passes through the center-of-gravity
point 60 (center-of-gravity point train 62) shown in FIGS. 13A
(13B) is added.
[0090] Furthermore, to enable horizontal changes in the position of
the center of gravity to be confirmed, object image data can be
displayed together with a vertical additional line that passes
through the position of the center of gravity in the object region
of the object image data. FIGS. 15A (15B) shows the case where a
vertical line 66 (vertical lines 67) that passes through the
center-of-gravity point 60 (center-of-gravity point train 62) shown
in FIGS. 13A (13B) is added.
[0091] In addition, a dispersion value concerning the position of
the center of gravity for each frame of time-series object image
data can be obtained and displayed. If the displayed dispersion
value is low, it is determined that changes in the position of the
center of gravity fall within a narrow range, therefore the golf
swing is stable. On the other hand, if the displayed dispersion
value is high, it is determined that the range of changes in the
position of the center of gravity is wide, therefore the golf swing
is unstable.
[0092] Part of an object region can be detected utilizing, for
example, color information contained in object image data. The
motion analysis section 24 horizontally scans object image data,
thereby generating a color histogram corresponding to each scanning
line. In a color histogram for a head region, torso region or leg
region, there is a tendency for a particular color to have a high
value. For example, in the head region, the color of a cap or hair
has a high value, while in the torso region, the color of clothing
on the upper half of the body has a high value. The motion analysis
section 24 detects, from the color histogram, a horizontal position
at which a characterizing change in color occurs, and divides the
object image at this horizontal position, thereby detecting a
partial region corresponding to the head region or torso region.
After that, the position of the center of gravity in each partial
image is computed in the same manner as described above, whereby an
image obtained by synthesizing each partial image with a
center-of-gravity point indicative of the position of the center of
gravity is displayed.
[0093] If the position of the center of gravity in each partial
region (head region, torso region, etc.) is obtained in the same
manner as in the entire object region (the entire player region),
it can be used as useful data. Further, if, for example, the tip of
a golf club is designated, the position of the club head is
automatically computed.
[0094] As described above, in the second embodiment, the position
of the center of gravity in an object region contained in extracted
time-series object image data is detected and used for the motion
analysis of an object corresponding to the object region. As a
result, the motion of the object contained in time-series image
data can be analyzed in a quantitatively manner.
[0095] (Third Embodiment)
[0096] FIG. 16 is a flowchart useful in explaining a motion
analysis step (step A3) employed in an image analysis method
according to a third embodiment. The third embodiment employs an
image analysis method that comprises a motion analysis step (step
A3) similar to that of the first embodiment. This motion analysis
step includes an object image data adjusting step (step B1) and
comparison synthesis step (step B2), which enables a quantitative
analysis of the motion of an object from images thereof contained
in time-series image data. At the step B1, second time-series
object image data extracted from second time-series image data,
which differs from first time-series object image data extracted
from first time-series image data at the object extracting step in
the first embodiment, is input and then adjusted so that it can be
compared with the first time-series object image data. At the step
B2, the first and second time-series object image data items are
displayed parallel.
[0097] Specifically, the first time-series object image data shown
in FIG. 17A and the second time-series object image data shown in
FIG. 17B are input to the object image data adjusting section 26,
where the object image data adjusting step (step B1) is
executed.
[0098] In general, when the motions of different objects are
photographed, the position or size differs between the objects, as
illustrated in FIGS. 17A and 17B. Therefore, if the swings of a
player assumed before and after lessons are compared, or if the
swing of an instructed person is compared with that of an
instructor, adjustment in position and/or size facilitates the
comparison and hence quantitative motion analysis.
[0099] At the object image data adjusting step (step B1), first and
second time-series object image data items are adjusted so that
they can be compared with each other. Specifically, the positions
and/or sizes of the objects are adjusted, the dominant hands of the
objects are adjusted (reverse of images), etc. FIGS. 18A and 18B
show examples of results of such adjustment.
[0100] The object in FIG. 17B is smaller than that of FIG. 17A and
is situated closer to the right-hand of the screen than that of
FIG. 17A. Therefore, the time-series object image data of FIG. 17B
is subjected to size enlargement and leftward shift processing,
thereby being adjusted to the time-series object image data of FIG.
18B that accords to the time-series object image data of FIG. 17A.
As a result, the two time-series object image data items shown in
FIGS. 18A and 18B can be easily compared, thereby facilitating the
quantitative motion analysis.
[0101] The object image data adjusting section 26 can also perform
time-based adjustment. For example, two time-series object image
data items indicative of respective golf swings can be adjusted in
time so that the ball-hitting moments during the swings can be
simultaneously displayed. Further, if two time-series object image
data items have different frame rates, a synthesizing technique,
such as morphing, is used to obtain synthesized images free from
time differences, which facilitates the comparison between the two
time-series object image data items.
[0102] Furthermore, it is not necessary to limit the first and
second time-series object image data items to object image data
items corresponding to different objects. They may be time-series
object image data items obtained by photographing one motion of a
single object in different directions.
[0103] Although in the above description, first and second
time-series object image data items corresponding to an object are
compared to thereby analyze the motion of the object, the same
analysis method as the above can be used for three or more
time-series object image data items.
[0104] After a first group of time-series object image data items
are adjusted to a second group of time-series object image data to
enable comparison therebetween at the object image data adjusting
step (step B1), the first group of adjusted time-series object
image data items are arranged in parallel with the second group of
adjusted time-series object image data items, and in each group,
the object image data items are superposed and synthesized. The
synthesized images of the two groups are displayed as object motion
analysis results on the display unit 14 as shown in FIG. 19.
[0105] As described above, in the third embodiment, a first group
of time-series object image data items are adjusted to a second
group of time-series object image data items to enable comparison
therebetween, and the first group of time-series object image data
items are synthesized and arranged in parallel with the second
group of time-series object image data items similarly synthesized,
thereby enabling a quantitative analysis of objects from images
thereof contained in different groups of time-series object image
data.
[0106] (Fourth Embodiment)
[0107] In the motion analysis step employed in the first
embodiment, extracted time-series object image data items can be
superposed and synthesized, while displaying, in the form of a line
and/or dots, the trajectory of a particular point in an object
region indicated by each extracted time-series object image data
item.
[0108] FIG. 20 is a flowchart useful in explaining a motion
analysis step employed in a fourth embodiment.
[0109] Firstly, the motion analysis section 24 detects, in a
particular region tracing step, a particular region from object
image data extracted by the object region extracting section 22.
For example, concerning object image data indicative of a golf
swing, the head of a golf club is detected as the particular
region. The head as the particular region is detected as the distal
portion of an object region in the object image data corresponding
to a golf club, or detected by directly pointing the head on the
screen using a pointing device such as a graphic tablet, as is
shown in FIG. 28 described later. The motion analysis section 24
detects the position of the particular region of each time-series
object image data item, thereby tracing changes in the position of
the particular region (step A3c1).
[0110] In a tracing result synthesizing step, the motion analysis
section 24 adds a line and/or dots indicative of the trajectory 70
of the particular region, superposes time-series object image data
items, and displays the resultant synthesis image on the display
unit 14 (step A3c2).
[0111] FIG. 21 illustrates a case where the head of a golf club is
considered to be a particular point in a golf swing, and
time-series object image data items are superposed on each other
and also synthesized with the trajectory of the particular point
displayed using a line and circular marks. As seen from FIG. 21,
the addition of the line and circular marks, indicative of the
trajectory 70, to time-series object image data facilitates the
understanding of the trajectory of the club head, and an analysis
concerning the dimension, symmetry, etc. of a swing.
[0112] The particular region can be detected by the following
method, as well as the above-described one. For example, the color
of the particular region is pre-stored, and the region with the
same color as the stored color is detected as a particular region
from object image data. In the above-described example, the color
of the club head is pre-registered, and the club head is detected
as a particular region. The color designated as a particular region
may be input by a user's manual operation through the input unit
12, or may be pointed on the display unit 14 using a pointing
device (see a seventh embodiment).
[0113] Alternatively, the initial frame of time-series image data
(object image data) may be displayed on the display unit 14,
thereby causing a user to designate the position (region) to be set
as a particular region on the frame by a manual operation. After
that, on the basis of the color of the designated region, the
particular region in the subsequent object image data may be
traced.
[0114] Further, a zone (expected zone) in the form of, for example,
a stripe, through which a particular region may pass, may be set in
an image, thereby determining, as the particular region, a region
of a predetermined size or more contained in the zone. In the case
of a golf swing, a curved stripe zone, through which the head of a
club is expected to pass, is set as an expected zone. The expected
zone may be designated by a user's manual operation using the input
unit 14 (pointing device), or may be predetermined to be a certain
distance from an object image.
[0115] Furthermore, in the display example of FIG. 21, the point
indicating a particular region and the line indicating the
trajectory of this point are synthesized. However, another type of
display can be employed. For example, an easily recognizable mark
(figure) may be synthesized with a particular region (e.g., a club
head). In addition, in FIG. 21, all items of time-series object
image data are synthesized, and further, the trajectory 70 of a
particular region extracted from each object image data item is
synthesized with the synthesized object image data items.
Alternatively, time-series object image data may be synthesized and
displayed in units of frames. In this case, each time new object
image data is synthesized and displayed, the object region in newly
synthesized object image data is connected by a line to that in
previously synthesized object image data, thereby generating the
trajectory 70.
[0116] Although in the above description, a particular region is
traced, a certain point in this region may be traced.
[0117] As described above, in the fourth embodiment, extracted
time-series object image data items are superposed and synthesized
while the trajectory of a particular point in an object region
indicated by each extracted object image data item is displayed
using a dot train or line, thereby enabling a quantitative analysis
of the motion of an object from images thereof contained in
time-series image data.
[0118] (Fifth Embodiment)
[0119] In the image analysis method for analyzing the movement of
an object from images thereof contained in time-series image data,
a change in a particular point in the object, such as the speed of
movement or the curvature of the trajectory of the object, is
computed from the movement of the particular point. For example,
the motion analysis section 24 detects, in the motion analysis
step, a particular point corresponding to a club head from object
image data extracted by the object region extracting section 22 (in
a manner similar to that employed in the fourth embodiment),
thereby computing the movement speed or the curvature of the
trajectory of the object from the detected particular point, and
displaying the computation result as an object motion analysis
result.
[0120] FIG. 22 is a flowchart useful in explaining a motion
analysis step employed in a fifth embodiment.
[0121] In a movement distance computing step (step A3d1), the
motion analysis section 24 computes, from time-series object image
data, the distance through which a particular region has moved. In
the subsequent movement speed computing step (step A3d2), the
motion analysis section 24 computes the movement speed of the
particular region from the movement distance of the particular
region computed at the movement distance computing step.
[0122] FIG. 23 is a view useful in explaining a method for
measuring the head speed of a club regarded as an important motion
analysis quantity in a golf swing motion. The head speed V is
obtained as V=L/T [m/sec], L representing the movement distance L
[m] of the club head within a predetermined time period T [sec].
The predetermined time period T [sec] in time-series object image
data depends upon the frame rate of a camera, and is generally
{fraction (1/30)} [sec]. If both even and odd field images are
used, the predetermined time period T [sec] is {fraction (1/60)}
[sec]. In either case, the predetermined time period T [sec] is a
known value.
[0123] The distance through which the club head (particular point)
moves corresponds to the distance between club head positions 72
and 73 in FIG. 23. Actually, the distance L through which the club
head moves corresponds to the length of the trajectory 74 shown in
FIG. 23. This can be computed from the club head positions 72 and
73.
[0124] Further, when the distance between club head positions of
different time points is short, there is no problem even if the
length 75 of the straight line that connects the club head
positions 72 and 73 is approximated as the distance through which
the club head moves. From the above, the distance L [m] through
which the club head moves within the predetermined time period T
[sec] is determined, thereby obtaining the head speed V=L/T
[m/sec].
[0125] In the movement distance computing step, the following
method can also be utilized.
[0126] For example, the motion analysis section 24 detects the
position of a ball in an image, and detects object image data items
that contain respective particular regions (indicating the club
head) closest to the ball position on the left and right sides. On
the basis of the positions of the particular regions of the two
object image data items, the movement distance of the particular
region is determined.
[0127] Alternatively, time-series object image data items may be
superposed and displayed on the display unit 14, and a user may
manually designate particular regions for computing the movement
distance, using the input unit 12 (pointing device).
[0128] Furthermore, an absolute movement speed may be computed from
a relative movement distance on an image.
[0129] For example, the height (cm) of a photographed player is
input, and the number of pixels corresponding to the height of the
player in an image (object image) is detected. On the basis of the
ratio between the number of pixels corresponding to the movement
distance of a particular region in the image, and the number of
pixels corresponding to the height of the player, the actual
movement distance (m) of the club head is computed. During a swing,
the player may bend down or kneel. Accordingly, the height of the
player in an image is lower than the actual height. To compensate
for this, a coefficient value may be preset and the height of the
player may be corrected beforehand using the coefficient value.
Instead of inputting the height (m) of a photographed player, the
height of an object region corresponding to the player may be
preset to a certain value, thereby computing the movement distance
of the club head in the same manner as the above. Further, instead
of an object region corresponding to a player, the movement
distance of the club head (particular region) may be computed in
the same manner as the above on the basis of a dimension (e.g. the.
length) of the club head in an image. In this case, the dimension
of the club head may be manually input by a user or may be set to a
predetermined value.
[0130] In addition, a predetermined object region may be detected
as a reference region from (e.g. the initial frame of) time-series
image data, thereby computing the movement distance of the club
head from the size of the reference region. For example, the region
corresponding to a ball in an image is detected as the reference
region. The golf ball has a predetermined size according to the
golf rules, and is usually colored in white. Accordingly, a white
circular region in an image is detected as a ball region. After
that, the number of pixels corresponding to the diameter of the
ball region is detected, whereby the actual movement distance (m)
of the club head is computed on the basis of the ratio between the
number of pixels corresponding to the diameter and the number of
pixels by which a particular region in the image has moved.
[0131] Also, when a golf swing is photographed at a predetermined
place, since the installation position of a camera and the position
of a ball can be fixed, the size of a region corresponding to the
ball in an image can be determined. On the basis of the
predetermined size of the region corresponding to the ball, the
movement distance of the club head can be computed in the same
manner.
[0132] FIG. 24 is a flowchart useful in explaining another motion
analysis step employed in the fifth embodiment.
[0133] Firstly, in a trajectory generating step, the motion
analysis section 24 generates, on the basis of time-series object
image data, a trajectory that indicates changes in the position of
a particular region (step A3e1). In the trajectory generating step,
the method described referring to the flowchart of FIG. 20 can be
utilized. In a subsequent curvature analysis step, the motion
analysis section 24 computes the curvatures of portions of the
trajectory generated at the trajectory generating step, thereby
performing a motion analysis based on the computed curvature.
Specifically, first and second curvatures corresponding to first
and second portions of the trajectory generated at the trajectory
generating step are computed, and performs a motion analysis on the
basis of the ratio between the first and second curvatures (step
A3e2).
[0134] FIG. 25 is a view useful in explaining a method for
measuring the curvature of a swing arc as an important motion
analysis quantity in a golf swing motion. The swing arc means an
arc along which the club head moves. In general, it is stated that
if the arc of a swing after the impact of the club and ball is
wider than that from the beginning of the swing to the impact, the
fly ball is stabilized and the carry is extended.
[0135] In light of this, the motion analysis section 24 computes,
from a synthesized image, the curvature R1 (first curvature) of a
first-half swing arc 80 and the curvature R2 (second curvature) of
a second-half swing arc 82.
[0136] Specifically, the motion analysis section 24, for example,
detects, as a reference point, a position in an object region,
about which the club is swung, and computes the curvature R1 of the
first-half swing arc 80 on the basis of the detected position of
rotation and particular points 83 and 84 corresponding to the
opposite ends of the first-half swing arc 80. Similarly, the motion
analysis section 24 computes the curvature R2 of the second-half
swing arc 82 on the basis of particular points 86 and 87
corresponding to the opposite ends of the second-half swing arc
82.
[0137] Although in the above description, the shoulder of a player
is used as a reference point, another position, such as the
position of the center of gravity computed from the initial frame
of an object image, may be used as the reference point. Further,
although in the above-described example, the swing arc before the
impact position is compared with that after the impact position,
the curvature ratio may be computed using any other portions of the
trajectory. For example, a user may manually designate any portions
of the trajectory in an image.
[0138] Further, the motion analysis section 24 can use the ratio
between the curvatures R1 and R2 of the first-half and second-half
swing arcs 80 and 82 as a parameter for quantitatively estimating
the degree of maturity of a golf swing.
[0139] It is advisable for a user to perform swing exercises to
establish a swing arc relationship of R1>>R2, while
confirming the motion analysis results of the motion analysis
section 24.
[0140] As described above, in the fifth embodiment, the movement
speed of a particular point in an object or the curvature of the
trajectory of the particular point is computed from the movement of
the particular point in the object, images of the object being
contained in time-series image data. As a result, the movement of
the object can be quantitatively analyzed.
[0141] Although the fifth embodiment employs the object image
extracting step (step A2) at which time-series object image data is
extracted, this step can be omitted if the trajectory of a
particular region is computed from time-series image data.
[0142] For example, the method (particular region tracing step)
described in the fourth embodiment is executed using time-series
image data as a target. This enables the trajectory of a particular
region corresponding to a club head to be obtained from time-series
image data. After that, the movement speed of the particular
region, the curvature of the trajectory of the region, and the
curvature ratio between portions of the trajectory can be computed
as in the fifth embodiment.
[0143] In this case, the motion analysis section 24 uses, for
example, the movement speed of the particular region, the curvature
of the trajectory of the region, and the curvature ratio as
estimation values for determining whether or not a golf swing is
good. The motion analysis section 24 compares the estimation values
with preset reference values, thereby estimating the motion of an
object image, i.e., the golf swing, and displaying an analysis
result based on the estimation.
[0144] For example, the golf swing may be estimated to be good if
the computed curvature ratio indicates that the first curvature R1
is greater than the second curvature R2, whereby a message
indicative of this estimation may be displayed on the display unit
14 as an analysis result.
[0145] (Sixth Embodiment)
[0146] In the image analysis method for analyzing the movement of
an object from images thereof contained in time-series image data,
the characterizing amount of a closed region defined by the
trajectory of a particular point in the object, such as the area of
the region, or the inclination of the trajectory, is computed from
the movement of the particular point. For example, the motion
analysis section 24 detects, in the motion analysis step, a
particular point from object image data extracted by the object
region extracting section 22 (in a manner similar to that employed
in the fourth embodiment), thereby obtaining a closed region
defined by the trajectory of the detected particular point, also
computing the area of the closed region or the inclination of the
trajectory, and displaying the computation result as an object
motion analysis result.
[0147] FIG. 26 is a flowchart useful in explaining another motion
analysis step employed in the sixth embodiment.
[0148] The motion analysis section 24 generates, in a trajectory
generating step, a trajectory indicative of changes in the position
of a particular region on the basis of time-series object image
data (step A3f1). The trajectory generating step can employ the
method illustrated in the flowchart of FIG. 2. In a trajectory area
analysis step, the motion analysis section 24 computes the area of
the region defined by the trajectory generated at the trajectory
generating step, and performs a motion analysis on the basis of the
computed area (step A3f2).
[0149] FIG. 27 is a view useful in explaining a method for
measuring the area and inclination of a swing plane as an important
motion analysis parameter in a golf swing motion. The swing plane
means a plane defined by the trajectory of a line segment that
moves during a golf swing motion, the line segment being defined by
the arms of a player, club shaft and club head (or club shaft and
club head).
[0150] In general, it is considered that the narrower (i.e., the
closer to a line) the swing plane when viewed from behind, the
better the swing.
[0151] In this embodiment, time-series data obtained by
photographing a swing from behind as shown in FIG. 27 is subjected
to an image analysis.
[0152] The motion analysis section 24 detects a particular point
corresponding to a club head from object image data extracted by
the object region extracting section 22 (in a manner similar to
that employed in the fourth embodiment), thereby obtaining a closed
region defined by the trajectory of the detected particular point,
i.e., the swing plane area 90 as shown in FIG. 27. The swing plane
area 90 may be detected from either the entire swing plane or a
portion of the swing plane. For example, in the case of FIG. 27,
assume that the detected swing plane area 90 is defined by the
trajectory of the aforementioned line segment during the motion of
Top.fwdarw.Down swing.fwdarw.Impact .fwdarw.Follow through. In
addition to the display of the swing plane area 90 as shown in FIG.
27, the motion analysis section 24 measures the area S of the swing
plane area 90 and displays the area S as an object motion analysis
result. The area S can be used as a parameter for quantitatively
estimating the degree of maturity of a golf swing.
[0153] It is advisable for a user to perform golf swing exercises
to make the area S of the swing plane area 90 closer to 0, while
confirming the motion analysis result of the motion analysis
section 24.
[0154] Further, the inclination of the swing plane in FIG. 27 is
another important motion analysis parameter in a golf swing motion.
If the inclination is close to 90.degree., the swing is called "an
upright swing". Further, if the inclination is too close to
0.degree., the swing is estimated to be bad. It is useful in a golf
motion analysis to measure the inclination and compare it with a
reference value.
[0155] The motion analysis section 24 detects, from particular
points contained in object image data, an uppermost particular
point 91 and lowermost particular point 92, for example, and
computes the inclination of the swing plane from the particular
points 91 and 92.
[0156] If it is detected that the trajectory of a particular point
has an intersection, it can be determined from the trajectory
whether the swing is an "outside-in" or "inside-out" swing.
Furthermore, when a swing of a right-handed player is photographed,
if the trajectory of a particular point travels clockwise, it is
detected that the swing is an "outside-in" swing, while if the
trajectory of a particular point travels counterclockwise, it is
detected that the swing is an "inside-out" swing (see FIG. 32).
This detection result can be displayed as a swing analysis
result.
[0157] As described above, in the sixth embodiment, the area of a
closed region defined by the trajectory of a particular point of an
object whose images are contained in time-series image data, or the
inclination of the trajectory is computed from the movement of the
particular point, thereby enabling the motion of the object to be
quantitatively analyzed.
[0158] Although the sixth embodiment employs the object image
extracting step (step A2) at which time-series object image data is
extracted, this step can be omitted if the trajectory of a
particular region is computed from time-series image data, and the
area of a closed region defined by the trajectory is computed.
[0159] For example, the method (particular region tracing step)
described in the fourth embodiment is executed using time-series
image data as a target. This enables the trajectory of a particular
region corresponding to a club head to be obtained from time-series
image data. After that, the area of the closed region defined by
the trajectory of the particular region can be computed as in the
sixth embodiment.
[0160] In this case, the motion analysis section 24 uses, for
example, the area of the closed region defined by the trajectory of
the particular region, as an estimation value for determining
whether or not a golf swing is good. The motion analysis section 24
compares the estimation value with a preset reference value,
thereby estimating the golf swing, and displaying an analysis
result based on the estimation.
[0161] (Seventh Embodiment)
[0162] In the time-series image data input step employed in the
first embodiment, in order to set the position of a particular
point used for a motion analysis, a characterizing color can be
beforehand attached to a particular point in an object to be
photographed, or the position of the particular point in the object
can be measured while picking up image data concerning the
object.
[0163] For example, if, in the time-series image data input section
20, the head of a golf club is marked in red and the globes of a
player are marked in yellow, the object region extracting section
22 can automatically detect a particular point from time-series
image data at the object region extracting step, by executing image
processing for detection of colors in an image.
[0164] The particular point detection can be performed without
image processing. For example, if time-series image data and range
data are simultaneously acquired using an instrument such as a
range finder, or if a motion capture device capable of position
detection is installed in a club head, a particular point in an
object can be detected.
[0165] In addition, a user can input a particular point in an
object if the object region extracting step performed by the object
region extracting section 22 in the first embodiment incorporates
an object image data generating step for extracting an object
region from time-series image data, and a particular point position
setting step for enabling users to designate and set the position
of a particular point in the extracted object region.
[0166] FIG. 28 is a view useful in explaining the particular point
position setting step. At the particular point position setting
step, the object region extracting section 22 causes an image (for
example, an initial frame image) for designating a particular point
to be displayed, as is shown in FIG. 28.
[0167] At this time, a user uses a pointing device, such as a mouse
or graphic tablet, to designate a particular point position in
object image data contained in an image. As a result, the object
region extracting section 22 inputs the particular point position
for a motion analysis. After that, the section 22 marks, in a
predetermined color (e.g., red), an object region corresponding to
the particular point position.
[0168] As described above, in the seventh embodiment, a particular
point position used for a motion analysis can be obtained from
time-series object image data, with the result that an object whose
images are contained in time-series image data can be analyzed
quantitatively.
[0169] (Eighth Embodiment)
[0170] FIG. 29 is a flowchart useful in explaining an image
analysis method according to an eighth embodiment. In FIG. 29, step
Cl is a time-series image data input step for inputting time-series
image data. Step C2 is an object region extracting step for
extracting an object region from the time-series image data and
generating time-series object image data. Step C3 is a motion
analysis step for analyzing the motion of an object on the basis of
shape information contained in the generated time-series object
image data. The steps C1-C3 are the same as the steps A1-A3
employed in the first to seventh embodiments. Further, step C4 is
an analysis result display step for digitizing an analysis result
and displaying it.
[0171] Firstly, at the time-series image data input step (step C1),
time-series image data is obtained by image pickup means such as a
video camera, and is input. Subsequently, at the object region
extracting step (step C2), an object region is extracted from the
time-series image data, thereby generating time-series object image
data. At the motion analysis step (step C3), a motion analysis of
an object is performed on the basis of the shape information
contained in the extracted time-series object image data. Lastly,
at the analysis result display step (step C4), the motion analysis
result obtained at the motion analysis step (step C3) is digitized
and displayed.
[0172] For example, as described in the second embodiment, in the
motion analysis method for detecting the position of the center of
gravity in an object region, vertical (or horizontal) changes in
the center of gravity can be displayed using a numerical value or
graph as shown in FIG. 30 at the analysis result display step by
the analysis result display section 30. In the example of FIG. 30,
there are provided an area 97 for displaying the highest position
of the center of gravity, and an area 98 for displaying a graph
indicative of vertical changes with time in the center of
gravity.
[0173] Further, also in the case of computing, from the movement of
a particular point of an object whose images are contained in
time-series image data, the movement speed of the particular point,
the curvature of the trajectory of the particular point, the area
of the closed region defined by the trajectory of the particular
point, and/or the inclination of the trajectory (swing plane) of
the particular point, the computed values can be displayed directly
or using a graph, as is described in the fifth and sixth
embodiments.
[0174] FIG. 31 illustrates a display example where the area S of
the swing plane area 90 is displayed on the display screen shown in
FIG. 27 that is used in the sixth embodiment, and an area 100 is
provided for displaying a swing estimation result (analysis result)
obtained on the basis of the area S.
[0175] To estimate a swing analysis result, personal data
concerning a player or golf club stored as time-series image data
may be pre-registered and used for a swing estimation. According
to, for example, the height of a player, the length of the hands of
the player, and/or the length of a golf club, the area S of the
swing plane area 90, the inclination of the swing plane, or the
shapes of swing arcs (first-half arc, second-half arc) as
estimation standards vary.
[0176] The analysis result display section 30 changes the
estimation standards in accordance with personal data, performs
estimation on the basis of the changed estimation standards, and
displays the estimation result in, for example, the area 100.
[0177] Further, the analysis result display section 30 may display
the trajectory of a particular point obtained by the motion
analysis section 24, in accordance with the direction of the
movement of the particular point. For example, as described in the
sixth embodiment, if the motion analysis section 24 has detected
that the trajectory of the particular point travels clockwise or
counterclockwise, therefore that the swing is an "inside-out" swing
or "outside-in" swing, the detection result is displayed as an
analysis result in the area 100.
[0178] Whether the swing is an "inside-out" swing or "outside-in"
swing is determined by analyzing changes in the position of the
particular point on the trajectory as shown in FIGS. 32A and 32B,
using the motion analysis section 24. FIGS. 32A and 32B are views
useful in explaining changes in the position (Top.fwdarw.Down
swing.fwdarw.Impact.fwdarw.Follo- w through) of the particular
point that appears in FIG. 31. In the case of FIG. 32A, the
particular point moves in the order of a1, a2, a3, a4, a5, a6 and
a1, i.e., it travels clockwise. In this case, the golf club is
swung down from the outside and is followed through to the inside.
Therefore, the swing is analyzed to be an "outside-in" swing. In
the case of FIG. 32B, the particular point moves in the order of
b1, b2, b3, b4, b5, b6 and b1, i.e., it travels counterclockwise.
In this case, the golf club is swung down from the inside and is
followed through to the outside. Therefore, the swing is analyzed
to be an "inside-out" swing.
[0179] If the motion analysis section 24 determines that the swing
is either an "inside-out" swing or an "outside-in" swing, the
analysis result display section 30 can display the swing plane area
90 in a color corresponding to the swing.
[0180] Also, the analysis result display section 30 can display the
trajectory during a "down swing" and that during a "follow through"
in respective colors.
[0181] As described above, in the eighth embodiment, the motion
analysis result of an object is displayed using a numerical value
or graph. Thus, the motion of the object can be quantitatively
analyzed from images thereof contained in time-series image
data.
[0182] The respective image analysis methods described in the first
to eighth embodiments can be combined appropriately.
[0183] Furthermore, the methods described in the above embodiments
can be written, as image analysis programs that can be executed in
a computer, to various recording mediums such as a magnetic disk
(flexible disk, hard disk, etc.), an optical disk (CD-ROM, DVD,
etc.), a semiconductor memory, etc., whereby the methods can be
provided for various apparatuses. In addition, the methods can be
transmitted to various apparatuses via a communication medium.
Computers for realizing the methods read an image analysis program
stored in a recording medium, or receives a program via a
communication medium, thereby performing the above-described
processes under the control of the program.
[0184] Additional advantages and modifications will readily occur
to those skilled in the art. Therefore, the invention in its
broader aspects is not limited to the specific details and
representative embodiments shown and described herein. Accordingly,
various modifications may be made without departing from the spirit
or scope of the general inventive concept as defined by the
appended claims and their equivalents.
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