U.S. patent application number 12/641788 was filed with the patent office on 2011-06-23 for motion detection using depth images.
This patent application is currently assigned to Microsoft Corporation. Invention is credited to Johnny Lee, Tommer Leyvand, Craig Peeper.
Application Number | 20110150271 12/641788 |
Document ID | / |
Family ID | 44151160 |
Filed Date | 2011-06-23 |
United States Patent
Application |
20110150271 |
Kind Code |
A1 |
Lee; Johnny ; et
al. |
June 23, 2011 |
MOTION DETECTION USING DEPTH IMAGES
Abstract
A sensor system creates a sequence of depth images that are used
to detect and track motion of objects within range of the sensor
system. A reference image is created and updated based on a moving
average (or other function) of a set of depth images. A new depth
images is compared to the reference image to create a motion image,
which is an image file (or other data structure) with data
representing motion. The new depth image is also used to update the
reference image. The data in the motion image is grouped and
associated with one or more objects being tracked. The tracking of
the objects is updated by the grouped data in the motion image. The
new positions of the objects are used to update an application. For
example, a video game system will update the position of images
displayed in the video based on the new positions of the objects.
In one implementation, avatars can be moved based on movement of
the user in front of a camera.
Inventors: |
Lee; Johnny; (Bellevue,
WA) ; Leyvand; Tommer; (Seattle, WA) ; Peeper;
Craig; (Kirkland, WA) |
Assignee: |
Microsoft Corporation
Redmond
WA
|
Family ID: |
44151160 |
Appl. No.: |
12/641788 |
Filed: |
December 18, 2009 |
Current U.S.
Class: |
382/103 |
Current CPC
Class: |
G06T 2207/10028
20130101; G06T 7/215 20170101; G06K 9/00335 20130101; G06T 7/254
20170101; G06K 9/00 20130101 |
Class at
Publication: |
382/103 |
International
Class: |
G06K 9/62 20060101
G06K009/62 |
Claims
1. A method for using depth images to sense motion, comprising:
creating a reference image that includes foreground data and
background data based on multiple previous depth images; receiving
a new depth image; creating a motion image based on the new depth
image and the reference image; identifying one or more objects in
the motion image; using position information for the identified one
or more objects to update an application; and updating the
reference image based on the new depth image.
2. The method of claim 1, wherein: the creating the motion image
includes subtracting the new depth image from the reference image
to create a set of difference data, identifying difference data
greater than a threshold as motion data that includes forward
motion data and backward motion data, and discarding the backward
motion data.
3. The method of claim 1, wherein: the identifying one or more
objects in the motion image includes grouping pixels of the motion
image to form one or more groups of pixels, associating each of the
one or more groups of pixels with one or more objects identified in
object history data, and updating the object history data; and the
using position information for the identified one or more objects
to update the application includes reporting the one or more
objects and positions for the one or more objects to the
application.
4. The method of claim 3, wherein: the grouping of pixels is based
on identifying connecting pixels.
5. The method of claim 3, wherein: the grouping of pixels is based
on proximity to previously identified objects.
6. The method of claim 1, wherein: the identifying one or more
objects in the motion image includes grouping pixels of the motion
image to form two or more groups of pixels, associating each of the
two or more groups of pixels with two or more objects identified in
object history data, and updating the object history data; and the
using position information for the identified one or more objects
to update the application includes reporting the two or more
objects and positions for the two or more objects to the
application.
7. The method of claim 1, wherein: the identifying one or more
objects in the motion image includes grouping pixels of the motion
image to an initial group of pixels, determining that the initial
group of pixels represents two objects, splitting the initial group
of pixels into a first group of pixels and a second group of
pixels, associating the first group of pixels with a first object
based on object history data, and associating the second group of
pixels with a second object based on object history data.
8. The method of claim 1, wherein: the identifying one or more
objects in the motion image includes grouping pixels of the motion
image to form two or more groups of pixels, predicting a trajectory
of an object and identifying the group of pixels closest to the
predicted trajectory.
9. The method of claim 1, wherein: the identifying one or more
objects in the motion image includes using object history data and
structure information about items in real space in order to
identify the one or more objects.
10. The method of claim 1, wherein: the updating the reference
image includes performing a weighted averaging function on the
reference image and the new depth image; and the creating the
reference image include performing the weighted averaging function
on previous depth images.
11. The method of claim 1, wherein: the using position information
for the identified one or more objects to update the application
includes changing the position of an image in a video display in
response to the position information.
12. The method of claim 1, wherein: the creating the motion image
includes subtracting the new depth image from the reference image
to create a set of difference data, identifying difference data
greater than a threshold as motion data that includes forward
motion data and backward motion data, and discarding the backward
motion data; the identifying one or more objects in the motion
image includes grouping pixels of the motion image to form one or
more groups of pixels, associating each of the one or more groups
of pixels with one or more objects identified in object history
data, and updating the object history data; the using position
information for the identified one or more objects to update the
application includes reporting the one or more objects and
positions for the one or more objects to the application; and the
using position information for the identified one or more objects
to update the application includes changing the position of an
image in a video display in response to the position
information.
13. An apparatus that uses depth images to sense motion,
comprising: a communication interface that receives depth images;
one or more storage devices that store depth images; a display
interface; and one or more processors in communication with the one
or more storage devices and the display interface, the one or more
processors access a new depth image received from the communication
interface and identify motion based comparing the new depth image
to a reference image stored in the one or more storage devices, the
one or more processors create a motion image representing
identified motion, the one or more processors group pixels of the
motion image and associate one or more groups of pixels with one or
more objects identified in object history data stored in the one or
more storage devices, the one or more processors use position
information for the identified one or more objects to update an
application running on the apparatus and provide signals on the
display interface that indicate the update to the application.
14. The apparatus of claim 13, wherein: the display interface
connects to a video monitor; the one or more processors use the
position information for the identified one or more objects to
update a position of an image on the video monitor; and the image
is displayed by the application.
15. The apparatus of claim 13, wherein: the reference image
includes foreground and background data based on multiple previous
depth images.
16. The apparatus of claim 13, wherein: the one or more processors
determine that a particular group of pixels represents two objects,
split the particular group of pixels into a first group of pixels
and a second group of pixels, associate the first group of pixels
with a first object based on object history data, and associate the
second group of pixels with a second object based on object history
data.
17. The apparatus of claim 13, wherein: the one or more processors
also use structure information about items in real space in order
to associate the one or more groups of pixels with the one or more
objects.
18. A method for using depth images to sense motion, comprising:
receiving a new depth image; identifying motion based on comparing
the new depth image to a reference image; creating a motion image
representing identified forward motion, and discarding identified
backward motion when creating the motion image; identifying one or
more objects in the motion image; and reporting the identified one
or more objects in the motion image.
19. The method of claim 18, further comprising: creating the
reference image that includes foreground data and background data
based on multiple previous depth images.
20. The method of claim 18, wherein: the reporting the identified
one or more objects includes reporting objects and positions of the
objects to an application that uses the position to create video.
Description
BACKGROUND
[0001] Many computing applications such as computer games,
multimedia applications, or the like use controls to allow users to
manipulate game characters or other aspects of an application.
Typically such controls are input using, for example, controllers,
remotes, keyboards, mice, or the like. Unfortunately, such controls
can be difficult to learn, thus creating a barrier between a user
and such games and applications. Furthermore, such controls may be
different than actual game actions or other application actions for
which the controls are used. For example, a game control that
causes a game character to swing a baseball bat may not correspond
to an actual motion of swinging the baseball bat.
SUMMARY
[0002] Disclosed herein are systems and methods for tracking motion
of a user or other objects in a scene using depth images. The
tracked motion is then used to update an application. Therefore, a
user can manipulate game characters or other aspects of the
application by using movement of the user's body and/or objects
around the user, rather than (or in addition to) using controllers,
remotes, keyboards, mice, or the like.
[0003] A sensor system creates a sequence of depth images that are
used to detect and track motion of objects within range of the
sensor system. A reference image is created and updated based on a
moving average (or other function) of a set of depth images. A new
depth images is compared to the reference image to create a motion
image, which is an image file (or other data structure) with data
representing motion. The new depth image is also used to update the
reference image. The data in the motion image is grouped and
associated with one or more objects being tracked. The tracking of
the objects is updated by the grouped data in the motion image. The
new positions of the objects are used to update an application. For
example, a video game system will update the position of images
displayed in the video based on the new positions of the objects.
In one implementation, avatars can be moved based on movement of
the user in front of a camera.
[0004] One embodiment includes creating a reference image that
includes foreground data and background data based on multiple
previous depth images, receiving a new depth image, creating a
motion image based on the new depth image and the reference image,
identifying one or more objects in the motion image, using position
information for the identified one or more objects to update an
application, and updating the reference image based on the new
depth image.
[0005] One embodiment includes a communication interface that
receives depth images, one or more storage devices that store depth
images, a display interface, and one or more processors in
communication with the one or more storage devices and the display
interface. The one or more processors access a new depth image
received from the communication interface and identify motion based
comparing the new depth image to a reference image stored in the
one or more storage devices. The one or more processors create a
motion image representing identified motion. The one or more
processors group pixels of the motion image and associate one or
more groups of pixels with one or more objects identified in object
history data stored in the one or more storage devices. The one or
more processors use position information for the identified one or
more objects to update an application running on the apparatus and
provide signals on the display interface that indicate the update
to the application.
[0006] One embodiment includes receiving a new depth image,
identifying motion based on comparing the new depth image to a
reference image, creating a motion image representing identified
forward motion and discarding identified backward motion when
creating the motion image, identifying one or more objects in the
motion image, and reporting the identified one or more objects in
the motion image.
[0007] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used as an aid in determining the scope of
the claimed subject matter. Furthermore, the claimed subject matter
is not limited to implementations that solve any or all
disadvantages noted in any part of this disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIGS. 1A and 1B illustrate an example embodiment of a
tracking system with a user playing a game.
[0009] FIG. 2 illustrates an example embodiment of a capture device
that may be used as part of the tracking system.
[0010] FIG. 3 illustrates an example embodiment of a computing
system that may be used to track motion and update an application
based on the tracked motion.
[0011] FIG. 4 illustrates another example embodiment of a computing
system that may be used to track motion and update an application
based on the tracked motion.
[0012] FIG. 5 is an example depth image.
[0013] FIG. 6 depicts the data in a depth image.
[0014] FIG. 7 is a flow chart describing one embodiment of a
process for capturing a sequence depth images.
[0015] FIG. 8 is a flow chart describing one embodiment of a
process for operating a computing system to track motion and update
an application based on that tracked motion.
[0016] FIG. 9 is a flow chart describing one embodiment of a
process for creating a motion image based on a depth image and a
reference image.
[0017] FIG. 10 is an example of a motion image.
[0018] FIG. 11 is a flow chart describing one embodiment of a
process for grouping pixels of a motion image.
[0019] FIG. 12 is a flow chart describing one embodiment of a
process for associating groups of pixels in a motion image with
objects being tracked.
[0020] FIG. 13 is a flow chart describing another embodiment of a
process for associating groups of pixels in a motion image with
objects being tracked.
DETAILED DESCRIPTION
[0021] Depth images are captured by a sensor and used by a
computing system to track motions of a user and/or other objects.
The tracked motion is then used to update an application.
Therefore, a user can manipulate game characters or other aspects
of the application by using movement of the user's body and/or
objects around the user, rather than (or in addition to) using
controllers, remotes, keyboards, mice, or the like. For example, a
video game system will update the position of images displayed in
the video based on the new positions of the objects or update an
avatar based on motion of the user.
[0022] FIGS. 1A and 1B illustrate an example embodiment of a
tracking system 10 with a user 18 playing a boxing video game. In
an example embodiment, the tracking system 10 may be used to
recognize, analyze, and/or track a human target such as the user 18
or other objects within range of tracking system 10.
[0023] As shown in FIG. 1A, tracking system 10 may include a
computing system 12. The computing system 12 may be a computer, a
gaming system or console, or the like. According to an example
embodiment, the computing system 12 may include hardware components
and/or software components such that computing system 12 may be
used to execute applications such as gaming applications,
non-gaming applications, or the like. In one embodiment, computing
system 12 may include a processor such as a standardized processor,
a specialized processor, a microprocessor, or the like that may
execute instructions stored on a processor readable storage device
for performing the processes described herein.
[0024] As shown in FIG. 1A, tracking system 10 may further include
a capture device 20. The capture device 20 may be, for example, a
camera that may be used to visually monitor one or more users, such
as the user 18, such that gestures and/or movements performed by
the one or more users may be captured, analyzed, and tracked to
perform one or more controls or actions within the application
and/or animate an avatar or on-screen character, as will be
described in more detail below.
[0025] According to one embodiment, the tracking system 10 may be
connected to an audiovisual device 16 such as a television, a
monitor, a high-definition television (HDTV), or the like that may
provide game or application visuals and/or audio to a user such as
the user 18. For example, the computing system 12 may include a
video adapter such as a graphics card and/or an audio adapter such
as a sound card that may provide audiovisual signals associated
with the game application, non-game application, or the like. The
audiovisual device 16 may receive the audiovisual signals from the
computing system 12 and may then output the game or application
visuals and/or audio associated with the audiovisual signals to the
user 18. According to one embodiment, the audiovisual device 16 may
be connected to the computing system 12 via, for example, an
S-Video cable, a coaxial cable, an HDMI cable, a DVI cable, a VGA
cable, component video cable, or the like.
[0026] As shown in FIGS. 1A and 1B, the tracking system 10 may be
used to recognize, analyze, and/or track a human target such as the
user 18. For example, the user 18 may be tracked using the capture
device 20 such that the gestures and/or movements of user 18 may be
captured to animate an avatar or on-screen character and/or may be
interpreted as controls that may be used to affect the application
being executed by computer environment 12. Thus, according to one
embodiment, the user 18 may move his or her body to control the
application and/or animate the avatar or on-screen character.
[0027] In the example depicted in FIGS. 1A and 1B, the application
executing on the computing system 12 may be a boxing game that the
user 18 is playing. For example, the computing system 12 may use
the audiovisual device 16 to provide a visual representation of a
boxing opponent 38 to the user 18. The computing system 12 may also
use the audiovisual device 16 to provide a visual representation of
a player avatar 40 that the user 18 may control with his or her
movements. For example, as shown in FIG. 1B, the user 18 may throw
a punch in physical space to cause the player avatar 40 to throw a
punch in game space. Thus, according to an example embodiment, the
computer system 12 and the capture device 20 recognize and analyze
the punch of the user 18 in physical space such that the punch may
be interpreted as a game control of the player avatar 40 in game
space and/or the motion of the punch may be used to animate the
player avatar 40 in game space.
[0028] Other movements by the user 18 may also be interpreted as
other controls or actions and/or used to animate the player avatar,
such as controls to bob, weave, shuffle, block, jab, or throw a
variety of different power punches. Furthermore, some movements may
be interpreted as controls that may correspond to actions other
than controlling the player avatar 40. For example, in one
embodiment, the player may use movements to end, pause, or save a
game, select a level, view high scores, communicate with a friend,
etc. According to another embodiment, the player may use movements
to select the game or other application from a main user interface.
Thus, in example embodiments, a full range of motion of the user 18
may be available, used, and analyzed in any suitable manner to
interact with an application.
[0029] In example embodiments, the human target such as the user 18
may have an object. In such embodiments, the user of an electronic
game may be holding the object such that the motions of the player
and the object may be used to adjust and/or control parameters of
the game. For example, the motion of a player holding a racket may
be tracked and utilized for controlling an on-screen racket in an
electronic sports game. In another example embodiment, the motion
of a player holding an object may be tracked and utilized for
controlling an on-screen weapon in an electronic combat game.
Objects not held by the user can also be tracked, such as objects
thrown, pushed or rolled by the user (or a different user) as well
as self propelled objects. In addition to boxing, other games can
also be implemented.
[0030] According to other example embodiments, the tracking system
10 may further be used to interpret target movements as operating
system and/or application controls that are outside the realm of
games. For example, virtually any controllable aspect of an
operating system and/or application may be controlled by movements
of the target such as the user 18.
[0031] FIG. 2 illustrates an example embodiment of the capture
device 20 that may be used in the tracking system 10. According to
an example embodiment, the capture device 20 may be configured to
capture video with depth information including a depth image that
may include depth values via any suitable technique including, for
example, time-of-flight, structured light, stereo image, or the
like. According to one embodiment, the capture device 20 may
organize the depth information into "Z layers," or layers that may
be perpendicular to a Z axis extending from the depth camera along
its line of sight.
[0032] As shown in FIG. 2, the capture device 20 may include an
image camera component 22. According to an example embodiment, the
image camera component 22 may be a depth camera that may capture a
depth image of a scene. The depth image may include a
two-dimensional (2-D) pixel area of the captured scene where each
pixel in the 2-D pixel area may represent a depth value such as a
distance in, for example, centimeters, millimeters, or the like of
an object in the captured scene from the camera.
[0033] As shown in FIG. 2, according to an example embodiment, the
image camera component 22 may include an infra-red (IR) light
component 24, a three-dimensional (3-D) camera 26, and an RGB
camera 28 that may be used to capture the depth image of a scene.
For example, in time-of-flight analysis, the IR light component 24
of the capture device 20 may emit an infrared light onto the scene
and may then use sensors (not shown) to detect the backscattered
light from the surface of one or more targets and objects in the
scene using, for example, the 3-D camera 26 and/or the RGB camera
28. In some embodiments, pulsed infrared light may be used such
that the time between an outgoing light pulse and a corresponding
incoming light pulse may be measured and used to determine a
physical distance from the capture device 20 to a particular
location on the targets or objects in the scene. Additionally, in
other example embodiments, the phase of the outgoing light wave may
be compared to the phase of the incoming light wave to determine a
phase shift. The phase shift may then be used to determine a
physical distance from the capture device to a particular location
on the targets or objects.
[0034] According to another example embodiment, time-of-flight
analysis may be used to indirectly determine a physical distance
from the capture device 20 to a particular location on the targets
or objects by analyzing the intensity of the reflected beam of
light over time via various techniques including, for example,
shuttered light pulse imaging.
[0035] In another example embodiment, the capture device 20 may use
a structured light to capture depth information. In such an
analysis, patterned light (i.e., light displayed as a known pattern
such as grid pattern, a stripe pattern, or different pattern) may
be projected onto the scene via, for example, the IR light
component 24. Upon striking the surface of one or more targets or
objects in the scene, the pattern may become deformed in response.
Such a deformation of the pattern may be captured by, for example,
the 3-D camera 26 and/or the RGB camera 28 and may then be analyzed
to determine a physical distance from the capture device to a
particular location on the targets or objects. In some
implementations, the IR Light component 24 is displaced from the
cameras 24 and 26 so triangulation can be used to determined
distance from cameras 24 and 26. In some implementations, the
capture device 20 will include a dedicated IR sensor to sense the
IR light.
[0036] According to another embodiment, the capture device 20 may
include two or more physically separated cameras that may view a
scene from different angles to obtain visual stereo data that may
be resolved to generate depth information. Other types of depth
image sensors can also be used to create a depth image.
[0037] The capture device 20 may further include a microphone 30.
The microphone 30 may include a transducer or sensor that may
receive and convert sound into an electrical signal. According to
one embodiment, the microphone 30 may be used to reduce feedback
between the capture device 20 and the computing system 12 in the
target recognition, analysis, and tracking system 10. Additionally,
the microphone 30 may be used to receive audio signals that may
also be provided by the user to control applications such as game
applications, non-game applications, or the like that may be
executed by the computing system 12.
[0038] In an example embodiment, the capture device 20 may further
include a processor 32 that may be in operative communication with
the image camera component 22. The processor 32 may include a
standardized processor, a specialized processor, a microprocessor,
or the like that may execute instructions including, for example,
instructions for receiving a depth image, generating the
appropriate data format (e.g., frame) and transmitting the data to
computing system 12.
[0039] The capture device 20 may further include a memory component
34 that may store the instructions that may be executed by the
processor 32, images or frames of images captured by the 3-D camera
and/or RGB camera, or any other suitable information, images, or
the like. According to an example embodiment, the memory component
34 may include random access memory (RAM), read only memory (ROM),
cache, Flash memory, a hard disk, or any other suitable storage
component. As shown in FIG. 2, in one embodiment, the memory
component 34 may be a separate component in communication with the
image capture component 22 and the processor 32. According to
another embodiment, the memory component 34 may be integrated into
the processor 32 and/or the image capture component 22.
[0040] As shown in FIG. 2, the capture device 20 may be in
communication with the computing system 12 via a communication link
36. The communication link 36 may be a wired connection including,
for example, a USB connection, a Firewire connection, an Ethernet
cable connection, or the like and/or a wireless connection such as
a wireless 802.11b, g, a, or n connection. According to one
embodiment, the computing system 12 may provide a clock to the
capture device 20 that may be used to determine when to capture,
for example, a scene via the communication link 36. Additionally,
the capture device 20 provides the depth information and visual
(e.g., RGB) images captured by, for example, the 3-D camera 26
and/or the RGB camera 28 to the computing system 12 via the
communication link 36. In one embodiment, the depth images and
visual images are transmitted at 30 frames per second. The
computing system 12 may then use the model, depth information, and
captured images to, for example, control an application such as a
game or word processor and/or animate an avatar or on-screen
character.
[0041] Computing system 12 includes gestures library 190, structure
data 192, depth image processing and object reporting module 194
and application 196. Depth image processing and object reporting
module 194 uses the depth images to track motion of objects, such
as the user and other objects. To assist in the tracking of the
objects, depth image processing and object reporting module 194
uses gestures library 190 and structure data 192.
[0042] Structure data 192 includes structural information about
objects that may be tracked. For example, a skeletal model of a
human may be stored to help understand movements of the user and
recognize body parts. Structural information about inanimate
objects may also be stored to help recognize those objects and help
understand movement.
[0043] Gestures library 190 may include a collection of gesture
filters, each comprising information concerning a gesture that may
be performed by the skeletal model (as the user moves). The data
captured by the cameras 26, 28 and the capture device 20 in the
form of the skeletal model and movements associated with it may be
compared to the gesture filters in the gesture library 190 to
identify when a user (as represented by the skeletal model) has
performed one or more gestures. Those gestures may be associated
with various controls of an application. Thus, the computing system
12 may use the gestures library 190 to interpret movements of the
skeletal model and to control application 196 based on the
movements. As such, gestures library may be used by depth image
processing and object reporting module 194 and application 196.
[0044] Application 196 can be a video game, productivity
application, etc. In one embodiment, depth image processing and
object reporting module 194 will report to application 196 an
identification of each object detected and the location of the
object for each frame. Applicant 196 will use that information to
update the position or movement of an avatar or other images in the
display.
[0045] FIG. 3 illustrates an example embodiment of a computing
system that may be the computing system 12 shown in FIGS. 1A-2 used
to track motion and/or animate (or otherwise update) an avatar or
other on-screen object displayed by an application. The computing
system such as the computing system 12 described above with respect
to FIGS. 1A-2 may be a multimedia console 100, such as a gaming
console. As shown in FIG. 3, the multimedia console 100 has a
central processing unit (CPU) 101 having a level 1 cache 102, a
level 2 cache 104, and a flash ROM (Read Only Memory) 106. The
level 1 cache 102 and a level 2 cache 104 temporarily store data
and hence reduce the number of memory access cycles, thereby
improving processing speed and throughput. The CPU 101 may be
provided having more than one core, and thus, additional level 1
and level 2 caches 102 and 104. The flash ROM 106 may store
executable code that is loaded during an initial phase of a boot
process when the multimedia console 100 is powered ON.
[0046] A graphics processing unit (GPU) 108 and a video
encoder/video codec (coder/decoder) 114 form a video processing
pipeline for high speed and high resolution graphics processing.
Data is carried from the graphics processing unit 108 to the video
encoder/video codec 114 via a bus. The video processing pipeline
outputs data to an A/V (audio/video) port 140 for transmission to a
television or other display. A memory controller 110 is connected
to the GPU 108 to facilitate processor access to various types of
memory 112, such as, but not limited to, a RAM (Random Access
Memory).
[0047] The multimedia console 100 includes an I/O controller 120, a
system management controller 122, an audio processing unit 123, a
network interface controller 124, a first USB host controller 126,
a second USB controller 128 and a front panel I/O subassembly 130
that are preferably implemented on a module 118. The USB
controllers 126 and 128 serve as hosts for peripheral controllers
142(1)-142(2), a wireless adapter 148, and an external memory
device 146 (e.g., flash memory, external CD/DVD ROM drive,
removable media, etc.). The network interface 124 and/or wireless
adapter 148 provide access to a network (e.g., the Internet, home
network, etc.) and may be any of a wide variety of various wired or
wireless adapter components including an Ethernet card, a modem, a
Bluetooth module, a cable modem, and the like.
[0048] System memory 143 is provided to store application data that
is loaded during the boot process. A media drive 144 is provided
and may comprise a DVD/CD drive, Blu-Ray drive, hard disk drive, or
other removable media drive, etc. The media drive 144 may be
internal or external to the multimedia console 100. Application
data may be accessed via the media drive 144 for execution,
playback, etc. by the multimedia console 100. The media drive 144
is connected to the I/O controller 120 via a bus, such as a Serial
ATA bus or other high speed connection (e.g., IEEE 1394).
[0049] The system management controller 122 provides a variety of
service functions related to assuring availability of the
multimedia console 100. The audio processing unit 123 and an audio
codec 132 form a corresponding audio processing pipeline with high
fidelity and stereo processing. Audio data is carried between the
audio processing unit 123 and the audio codec 132 via a
communication link. The audio processing pipeline outputs data to
the A/V port 140 for reproduction by an external audio player or
device having audio capabilities.
[0050] The front panel I/O subassembly 130 supports the
functionality of the power button 150 and the eject button 152, as
well as any LEDs (light emitting diodes) or other indicators
exposed on the outer surface of the multimedia console 100. A
system power supply module 136 provides power to the components of
the multimedia console 100. A fan 138 cools the circuitry within
the multimedia console 100.
[0051] The CPU 101, GPU 108, memory controller 110, and various
other components within the multimedia console 100 are
interconnected via one or more buses, including serial and parallel
buses, a memory bus, a peripheral bus, and a processor or local bus
using any of a variety of bus architectures. By way of example,
such architectures can include a Peripheral Component Interconnects
(PCI) bus, PCI-Express bus, etc.
[0052] When the multimedia console 100 is powered ON, application
data may be loaded from the system memory 143 into memory 112
and/or caches 102, 104 and executed on the CPU 101. The application
may present a graphical user interface that provides a consistent
user experience when navigating to different media types available
on the multimedia console 100. In operation, applications and/or
other media contained within the media drive 144 may be launched or
played from the media drive 144 to provide additional
functionalities to the multimedia console 100.
[0053] The multimedia console 100 may be operated as a standalone
system by simply connecting the system to a television or other
display. In this standalone mode, the multimedia console 100 allows
one or more users to interact with the system, watch movies, or
listen to music. However, with the integration of broadband
connectivity made available through the network interface 124 or
the wireless adapter 148, the multimedia console 100 may further be
operated as a participant in a larger network community.
[0054] When the multimedia console 100 is powered ON, a set amount
of hardware resources are reserved for system use by the multimedia
console operating system. These resources may include a reservation
of memory (e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking
bandwidth (e.g., 8 kbs), etc. Because these resources are reserved
at system boot time, the reserved resources do not exist from the
application's view.
[0055] In particular, the memory reservation preferably is large
enough to contain the launch kernel, concurrent system applications
and drivers. The CPU reservation is preferably constant such that
if the reserved CPU usage is not used by the system applications,
an idle thread will consume any unused cycles.
[0056] With regard to the GPU reservation, lightweight messages
generated by the system applications (e.g., popups) are displayed
by using a GPU interrupt to schedule code to render popup into an
overlay. The amount of memory required for an overlay depends on
the overlay area size and the overlay preferably scales with screen
resolution. Where a full user interface is used by the concurrent
system application, it is preferable to use a resolution
independent of application resolution. A scaler may be used to set
this resolution such that the need to change frequency and cause a
TV resynch is eliminated.
[0057] After the multimedia console 100 boots and system resources
are reserved, concurrent system applications execute to provide
system functionalities. The system functionalities are encapsulated
in a set of system applications that execute within the reserved
system resources described above. The operating system kernel
identifies threads that are system application threads versus
gaming application threads. The system applications are preferably
scheduled to run on the CPU 101 at predetermined times and
intervals in order to provide a consistent system resource view to
the application. The scheduling is to minimize cache disruption for
the gaming application running on the console.
[0058] When a concurrent system application requires audio, audio
processing is scheduled asynchronously to the gaming application
due to time sensitivity. A multimedia console application manager
(described below) controls the gaming application audio level
(e.g., mute, attenuate) when system applications are active.
[0059] Input devices (e.g., controllers 142(1) and 142(2)) are
shared by gaming applications and system applications. The input
devices are not reserved resources, but are to be switched between
system applications and the gaming application such that each will
have a focus of the device. The application manager preferably
controls the switching of input stream, without knowledge the
gaming application's knowledge and a driver maintains state
information regarding focus switches. The cameras 26, 28 and
capture device 20 may define additional input devices for the
console 100 via USB controller 126 or other interface.
[0060] FIG. 4 illustrates another example embodiment of a computing
system 220 that may be the computing system 12 shown in FIGS. 1A-2
used to track motion and/or animate (or otherwise update) an avatar
or other on-screen object displayed by an application. The
computing system environment 220 is only one example of a suitable
computing system and is not intended to suggest any limitation as
to the scope of use or functionality of the presently disclosed
subject matter. Neither should the computing system 220 be
interpreted as having any dependency or requirement relating to any
one or combination of components illustrated in the exemplary
operating system 220. In some embodiments the various depicted
computing elements may include circuitry configured to instantiate
specific aspects of the present disclosure. For example, the term
circuitry used in the disclosure can include specialized hardware
components configured to perform function(s) by firmware or
switches. In other examples embodiments the term circuitry can
include a general purpose processing unit, memory, etc., configured
by software instructions that embody logic operable to perform
function(s). In example embodiments where circuitry includes a
combination of hardware and software, an implementer may write
source code embodying logic and the source code can be compiled
into machine readable code that can be processed by the general
purpose processing unit. Since one skilled in the art can
appreciate that the state of the art has evolved to a point where
there is little difference between hardware, software, or a
combination of hardware/software, the selection of hardware versus
software to effectuate specific functions is a design choice left
to an implementer. More specifically, one of skill in the art can
appreciate that a software process can be transformed into an
equivalent hardware structure, and a hardware structure can itself
be transformed into an equivalent software process. Thus, the
selection of a hardware implementation versus a software
implementation is one of design choice and left to the
implementer.
[0061] Computing system 220 comprises a computer 241, which
typically includes a variety of computer readable media. Computer
readable media can be any available media that can be accessed by
computer 241 and includes both volatile and nonvolatile media,
removable and non-removable media. The system memory 222 includes
computer storage media in the form of volatile and/or nonvolatile
memory such as read only memory (ROM) 223 and random access memory
(RAM) 260. A basic input/output system 224 (BIOS), containing the
basic routines that help to transfer information between elements
within computer 241, such as during start-up, is typically stored
in ROM 223. RAM 260 typically contains data and/or program modules
that are immediately accessible to and/or presently being operated
on by processing unit 259. By way of example, and not limitation,
FIG. 4 illustrates operating system 225, application programs 226,
other program modules 227, and program data 228.
[0062] The computer 241 may also include other
removable/non-removable, volatile/nonvolatile computer storage
media. By way of example only, FIG. 4 illustrates a hard disk drive
238 that reads from or writes to non-removable, nonvolatile
magnetic media, a magnetic disk drive 239 that reads from or writes
to a removable, nonvolatile magnetic disk 254, and an optical disk
drive 240 that reads from or writes to a removable, nonvolatile
optical disk 253 such as a CD ROM or other optical media. Other
removable/non-removable, volatile/nonvolatile computer storage
media that can be used in the exemplary operating environment
include, but are not limited to, magnetic tape cassettes, flash
memory cards, digital versatile disks, digital video tape, solid
state RAM, solid state ROM, and the like. The hard disk drive 238
is typically connected to the system bus 221 through an
non-removable memory interface such as interface 234, and magnetic
disk drive 239 and optical disk drive 240 are typically connected
to the system bus 221 by a removable memory interface, such as
interface 235.
[0063] The drives and their associated computer storage media
discussed above and illustrated in FIG. 4, provide storage of
computer readable instructions, data structures, program modules
and other data for the computer 241. In FIG. 4, for example, hard
disk drive 238 is illustrated as storing operating system 258,
application programs 257, other program modules 256, and program
data 255. Note that these components can either be the same as or
different from operating system 225, application programs 226,
other program modules 227, and program data 228. Operating system
258, application programs 257, other program modules 256, and
program data 255 are given different numbers here to illustrate
that, at a minimum, they are different copies. A user may enter
commands and information into the computer 241 through input
devices such as a keyboard 251 and pointing device 252, commonly
referred to as a mouse, trackball or touch pad. Other input devices
(not shown) may include a microphone, joystick, game pad, satellite
dish, scanner, or the like. These and other input devices are often
connected to the processing unit 259 through a user input interface
236 that is coupled to the system bus, but may be connected by
other interface and bus structures, such as a parallel port, game
port or a universal serial bus (USB). The cameras 26, 28 and
capture device 20 may define additional input devices for the
console 100 that connect via user input interface 236. A monitor
242 or other type of display device is also connected to the system
bus 221 via an interface, such as a video interface 232. In
addition to the monitor, computers may also include other
peripheral output devices such as speakers 244 and printer 243,
which may be connected through a output peripheral interface 233.
Capture Device 20 may connect to computing system 220 via output
peripheral interface 233, network interface 237, or other
interface.
[0064] The computer 241 may operate in a networked environment
using logical connections to one or more remote computers, such as
a remote computer 246. The remote computer 246 may be a personal
computer, a server, a router, a network PC, a peer device or other
common network node, and typically includes many or all of the
elements described above relative to the computer 241, although
only a memory storage device 247 has been illustrated in FIG. 4.
The logical connections depicted include a local area network (LAN)
245 and a wide area network (WAN) 249, but may also include other
networks. Such networking environments are commonplace in offices,
enterprise-wide computer networks, intranets and the Internet.
[0065] When used in a LAN networking environment, the computer 241
is connected to the LAN 245 through a network interface or adapter
237. When used in a WAN networking environment, the computer 241
typically includes a modem 250 or other means for establishing
communications over the WAN 249, such as the Internet. The modem
250, which may be internal or external, may be connected to the
system bus 221 via the user input interface 236, or other
appropriate mechanism. In a networked environment, program modules
depicted relative to the computer 241, or portions thereof, may be
stored in the remote memory storage device. By way of example, and
not limitation, FIG. 4 illustrates application programs 248 as
residing on memory device 247. It will be appreciated that the
network connections shown are exemplary and other means of
establishing a communications link between the computers may be
used.
[0066] As explained above, capture device 20 provides RGB images
and depth images to computing system 12. The depth image may be a
plurality of observed pixels where each observed pixel has an
observed depth value. For example, the depth image may include a
two-dimensional (2-D) pixel area of the captured scene where each
pixel in the 2-D pixel area may have a depth value such as a length
or distance in, for example, centimeters, millimeters, or the like
of an object in the captured scene from the capture device.
[0067] FIG. 5 illustrates an example embodiment of a depth image
that may be received at computing system 12 from capture device 20.
According to an example embodiment, the depth image may be an image
and/or frame of a scene captured by, for example, the 3-D camera 26
and/or the RGB camera 28 of the capture device 20 described above
with respect to FIG. 2. As shown in FIG. 5, the depth image may
include a human target corresponding to, for example, a user such
as the user 18 described above with respect to FIGS. 1A and 1B and
one or more non-human targets such as a wall, a table, a monitor,
or the like in the captured scene. As described above, the depth
image may include a plurality of observed pixels where each
observed pixel has an observed depth value associated therewith.
For example, the depth image 400 may include a two-dimensional
(2-D) pixel area of the captured scene where each pixel at
particular X-value and Y-value in the 2-D pixel area may have a
depth value such as a length or distance in, for example,
centimeters, millimeters, or the like of a target or object in the
captured scene from the capture device.
[0068] In one embodiment, the depth image may be colorized or
grayscale such that different colors or shades of the pixels of the
depth image correspond to and/or visually depict different
distances of the targets 404 from the capture device 20. Upon
receiving the image, one or more high-variance and/or noisy depth
values may be removed and/or smoothed from the depth image;
portions of missing and/or removed depth information may be filled
in and/or reconstructed; and/or any other suitable processing may
be performed on the received depth image.
[0069] FIG. 6 provides another view/representation of a depth image
(not corresponding to the same example as FIG. 5). The view of FIG.
6 shows the depth data for each pixel as an integer that represents
the distance of the target to capture device 20 for that pixel. The
example depth image of FIG. 6 shows 24.times.24 pixels; however, it
is likely that a depth image of greater resolution would be
used.
[0070] FIG. 7 is a flow chart describing one embodiment of a
process for operating capture device 20. In step 402, a depth image
and a visual image are captured by any of the sensors in capture
device 20 described herein, or other suitable sensors known in the
art. In one embodiment, the depth image is captured separately from
the visual image. In some implementations, the depth image and
visual image are captured at the same time, while in other
implementations they are captured sequentially or at different
times. In other embodiments, the depth image is captured with the
visual image or combined with the visual image as one image file so
that each pixel has an R value, a G value, a B value and a Z value
(distance). In step 404, the depth image and the visual image are
transmitted to computing system 12. In one embodiment, the depth
image and visual image are transmitted at 30 frames per second. In
some examples, the depth image is transmitted separately from the
visual image. In other embodiments, the depth image and visual
image can be transmitted together.
[0071] FIG. 8 is a flowchart describing one embodiment of a process
for operating computing system 12 to use depth images to track and
identify objects (users and other objects) in order to update an
application based on the objects identified and tracked. The
process of FIG. 8 is performed in response to capturing device 20
transmitting a depth image and visual image to computing system 12
(step 404 of FIG. 7); therefore, the process of FIG. 8 is performed
many times. In one embodiment, the process of FIG. 8 is performed
30 times a second. In other embodiments, the process of FIG. 8 can
be performed more or less than 30 times per second based on or
independent from the frame rate of the depth images. In step 460 of
FIG. 8, computing system 12 will receive a new depth image from
capturing device 20 in response to step 404 of FIG. 7. This depth
image is provided to the depth image processing and object
reporting module 194.
[0072] In step 462 of FIG. 8, a motion image is created based on
the newly received depth image and a reference image. The reference
image is typically in the same format as the depth image. In one
embodiment, the reference image as a single static image of a scene
taken prior to the motion. In another embodiment, the reference
image is a moving average of the most recent N frames of depth
images which provides an estimate of the typical depth position for
every pixel in the depth image sequence. If the reference image is
a moving average, then the reference image will update over time.
The average can either be a uniform average across the sequence
length, an exponential decay average, or other weighted average
using an external weighting system. Comparing the current depth
image to the moving average allows the system to adapt to gradual
changes in the scene, as well as automatically adapt to a moving
capture device without a reinitialization step. In one embodiment,
the reference image is based on 4 seconds of depth images;
therefore, if the frame rate is 30 frames per second then the
reference image is based on the most recent 120 depth images.
[0073] The reference image is updated based on each new depth image
is received. In one example, it will take 120 frames before the
reference image is established. In other embodiments, the reference
image will be established with the first depth image and then each
additional depth image will be added to the reference image until
there are 120 depth images received, then the reference image will
be updated by the most recent 120 images.
[0074] The Equation (1) below provides one example of a formula for
creating a reference image:
new average - t * ( old average ) + new data t + 1 Equation ( 1 )
##EQU00001##
[0075] Equation (1) is used to operate on each pixel of the
reference image. The variable "t" is the number of frames included
in the reference image. In one example, 120 frames are included (4
seconds of video at 30 frames per second). The variable "old
average" is the pixel value in the reference image for the
particular pixel under consideration. The variable "new data" is
the corresponding pixel value in the new depth image received. The
output "new average" is the new pixel value of the updated
reference image. Equation 1 is performed for every pixel of the
reference image. In this manner, the reference image is re-created
each time it is updated.
[0076] In one alternative, the value of t in Equation (1) can be
different if the motion is backward versus forward. For a
particular pixel, the value of t can be 120 if the motion is
forward and the value of t can be 30 if the motion is backward.
[0077] When using Equation (1), the system does not need to keep a
buffer of the previous 120 frames of depth images. Only the current
new depth image needs to be stored in a buffer as well as the
reference image. If the system used a straight averaging process,
then a buffer would need to keep the last 120 frames of depth
images.
[0078] Step 462 includes creating a motion image based on the new
depth image received in step 460 and the reference image discussed
above. As explained above, in one embodiment, the depth image and
the reference image have the same number of pixels. The motion
image created in step 462 is also a file (or other data structure)
with the same number of pixels in the same format as the depth
image and reference image. In one embodiment, the motion image is
created by subtracting the new depth image from the reference image
on a pixel by pixel basis. Thus, a corresponding pixel in the depth
image is subtracted from the corresponding pixel in the reference
image and the result is stored as the corresponding pixel the
motion image.
[0079] If the pixel value in the new depth image is the same as the
pixel value in the reference image, then there is no motion
detected for the pixel. If the difference between the reference
image and the new depth image is positive, then there is motion
towards capture device 20. If the difference between the reference
image and the depth image is negative, then there is motion away
from the capture device 20.
[0080] In one embodiment, the process for creating the motion image
will compare a threshold to the difference between the reference
image and new depth image, on a pixel-by-pixel basis, so that small
variations will not be detected as motion. Additionally, some
embodiments will discard backward motion data (away from the
camera) and only report forward motion (toward the camera). In some
implementation, the system will track the magnitude of the motion
(e.g., the difference between the reference image pixel and depth
image pixel), while in other embodiments, the system will only
store a Boolean value in the motion image to indicate whether there
is motion or not.
[0081] FIG. 9 is a flowchart describing one embodiment or a process
for creating a motion image based on the new depth image received
and the reference image that includes subtracting pixels, using a
threshold and discarding backward motion. Thus, the process of FIG.
9 is one example implementation of step 462 of FIG. 8. In step 500
of FIG. 9, the system will access a pixel that has not already been
operated on in the depth image. In step 502, the system will access
the corresponding pixel in the reference image. In step 504, the
system will subtract the pixels by subtracting the pixel in the new
depth image from the pixel in the reference image. If the resulting
magnitude is negative, then a zero is inserted into the
corresponding pixel in the motion image in step 514 because the
system is discarding backward motion. If the difference in pixels
was not negative, then in step 508 it is determined whether the
difference is greater than a threshold (e.g. 2 centimeters per
meter). If the difference is not greater than the threshold, then
in step 514 a zero is added to the corresponding pixel in the
motion image because the difference is not great enough to be
classified as forward motion. This technique helps reduce noise. If
the difference is greater than the threshold, then the system will
conclude that there is motion if forward motion (e.g., toward the
camera), and in step 510, the difference data is inserted in
corresponding pixel in the motion image. In another embodiment,
rather than adding the difference data, a Boolean value (e.g., 1)
is added to the motion image in step 510. In step 512, it is
determined whether there are more pixels in the depth image that
need to be operated on. If there are no more pixels in the depth
image to be operated on, then the process of FIG. 9 is complete. If
there are more pixels in the depth image to operate on, the process
moves back to step 500 and accesses the next pixel in the depth
image that has not been operated on. At the end of the process of
FIG. 9, the motion image is created and includes either a 1 or 0 at
each pixel. A zero means no motion (or discarded backward motion).
A 1 for a pixel value indicates that there was forward motion for
that pixel.
[0082] FIG. 10 shows one example of a motion image created using
the process of FIG. 9. As can be seen, some of the pixels have a
data value 1 indicating that those pixels represent forward motion.
The pixels for which there is no motion would have data zero;
however, to make the drawing easier to read, the zeros have been
left blank.
[0083] In the above discussion, the comparison between the newly
received depth image and the reference image is a simple
subtracting and thresholding of values. More sophisticated
embodiments may use mean squared error, standard deviation,
difference of means or other statistical measures to compare the
two data sets. This comparison may be done at the image level,
pixel level or some other intermediate granularity of the
image.
[0084] In the above discussion, there was only one reference image
that was maintained and compared against the newly received depth
images. In other embodiments, the system can use more than one
reference image. For example, the system can create and maintain
two or more reference images that averaged the depth data over
differing, or even randomized, time constants. Comparison against
multiple reference images can increase likelihood that moving
objects will be properly identified. In such an embodiment, the new
depth image is compared against multiple reference images. Any
motion detected from any other comparisons will be used to add a 1
to the appropriate pixel in the created motion image. Other schemes
for comparing multiple reference images to a depth image can also
be used.
[0085] Looking back at FIG. 8, after the motion image is created in
step 462, the reference image is updated in step 464 based on the
new depth image that was just received in the most recent iteration
of step 460. As discussed above, in one embodiment, the reference
image is a moving average of the most recent N frames of depth
images. Since the new depth image is received, the average must
move based on the newly received frame. Therefore, Equation (1) is
used on each pixel of the reference image with "old average" being
the data from the existing reference image and "new data" being the
data from the newly received depth image. Equation (1) is performed
for each pixel of the reference image to update the existing
reference image to create a newly updated reference image in step
464.
[0086] Once regions of the depth image have been identified as
moving, it is useful to segment them into individual groups and
track their locations over time. If multiple objects are detected
by the system, the output is a collection of pixels that have been
identified as moving. To group these pixels into individual
objects, the system can use a method of segmentation or grouping
called connected component analysis. Neighboring pixels that are
also identified as moving are considered connected and therefore
part of the same group. Once all of the pixels have been accounted
for, the result is a set of groups that represent potential moving
objects in the scene. Alternative methods determining which pixels
of part of the same group can also be used such as thresholding
Euclidian 3D distance or surface distance. Another alternative is
to use clustering methods where a fixed number of groups are
hypothesized, pixels are associated with each hypothetical group,
and then the overall hypothesis is scored based on how well it
explained the data. Another method is to maintain the probability
that a pixel belongs to each possible group rather than directly
associating it with a single group. This may be valuable in
scenarios where the tracking system that maintains group assignment
between frames can handle ambiguity in pixel association making it
more robust in some application scenarios.
[0087] Looking back at FIG. 8, step 466 of FIG. 8 includes grouping
the pixels of the motion image that represent motion (e.g., store
data 1). In one embodiment, the pixels are grouped based on
proximity to each other. FIG. 11 is a flowchart describing one
embodiment of a process for grouping pixels of motion image based
on proximity. Thus, FIG. 8 is one example implementation of step
466 of FIG. 8. In step 702, the system will access an ungrouped
pixel in the motion image that indicates motion (e.g., has data 1).
In step 704, the system will identify all connected pixels that
include the pixel accessed in step 702 as one group. For example,
if a pixel has the data 1, the system looks for all pixels that
neighbor that pixel that are also data 1, and of those neighbors
with data 1, the system will look for all their neighboring pixels
that also have data 1, and so on, until all connected contiguous
and continuous pixels showing data 1 are grouped into a group. Then
in step 706, the system determines whether there are any ungrouped
pixels that have not been considered yet. If so, the process moves
back to step 702 and accesses another ungrouped pixel and attempts
to find connected pixels. If, in step 706, it is determined that
there are no more ungrouped pixels, then in step 708, the system
will identify the center of each group. When step 708 is performed,
each of the pixels is in a group. It is possible that some groups
will only have one pixel. For groups that have more than one pixel,
the system will determine the geometric center of the group and
identify the x and y coordinate (in pixel space) of that group.
Note that FIG. 10 shows the motion image with the pixels grouped.
For example, a first set of pixels are grouped as depicted by oval
602 and a second set of pixels are grouped as depicted by oval
604.
[0088] Groups containing a small number of pixels may be the result
of noise in the depth image that exceeds the threshold limit in the
motion detection step. To further filter out interference from
noise, one embodiment may require a minimum pixel count or minimum
physical size of a group to perform further motion analysis.
[0089] Looking back at FIG. 8, after the pixels of the motion image
are grouped in step 466, the groups are associated with objects
being tracked in step 468. It is often valuable to track those
objects over time and build an understanding that a moving object
in one image is the same object in the following image. In one
embodiment, the system will create object history data which stores
information about the motion of objects being tracked. For example,
this object history data can store an identification of all of the
various objects being tracked and prior positions of the objects in
the motion image. There are many methods for tracking the objects
that can be used. No one particular method for tracking is
required. In one example, a spatial likelihood tracking approach
can be used that records the positions of each object over time and
reassociates new motion with whatever object it is most likely to
be. One example is to associate movement with the closest
identified object in the previous image in the sequence.
Alternative embodiments include predicting the trajectory of the
moving object based on recent movements. The system can also model
movement when the object is known to be a subcomponent of a larger
object, such as an arm or a larger body.
[0090] FIGS. 12 and 13 provide two embodiments of processes for
associating groups of pixels in the motion image with objects being
tracked (step 468 of FIG. 8). The process of FIG. 12 associates
groups of pixels with objects by associating a group with the
closest identified object in the previous image in the sequence. In
step 804 of FIG. 12, the system will access the object history data
discussed above. Thus, the system will have information about all
the objects previously tracked and their positions in the previous
motion images. The object history data can be stored in any
suitable type of data structure. In step 806, the system will
attempt to associate the center of each group in the current motion
image with the object of closest proximity in the most recent
motion image. In some embodiments, there will be a threshold
distance so that the association must be reasonable. For example,
if the closest object is halfway across the image, that may not be
a reasonable association and will be discarded. The particular
threshold used for the association will vary based on
implementation and experimentation.
[0091] In some embodiments, the depth image process and client
reporting module 194 performing the association of step 840 will
make use of the information in gestures library 190 or a structure
data 192 to associate groups with objects. For example, based on
known shapes, the system can correlate a group with an existing
object. If an object being tracked is a person, external structure
data can be used to identify the shape of a person which will help
the system better associate a group of pixels in the motion image
with the person. Additionally, if the system knows it has
previously been tracking a person with an arm moving, the external
structure data 192 and the gestures library 190 could teach the
system about probable movement of an arm, leg or other body part so
the system can more readily identify the object. Similarly, the
system may be able to recognize an inanimate object such as a ball
or tennis racket based on references or templates in structure data
192.
[0092] Step 806 attempts to assign every group to an object being
tracked. In some embodiments, some groups may not be assignable. In
one embodiment, any group that cannot be assigned to an object will
be assumed to be a new object. In step 808, any unassociated groups
have new objects created and these unassociated groups are assigned
to the new objects.
[0093] If moving objects come into close proximity to each other
they may appear to merge into a single region. In some
applications, it may be desirable to try to separate the single
region back into the individual objects based on previous
observation. One embodiment includes segmenting the pixels based on
their proximity to the center of the previous objects. Pixels are
associated with whatever previous objects they were closest to. The
distance metric may be as simple as Euclidian distance, surface
distance or other representation of distance.
[0094] In step 810, the system determines whether two objects from
a previous motion image have merged in the current motion image.
That is, if there are two objects in the previous motion image and
the current image has only one object in a similar or proximal
location as the two objects in the previous image, the system can
determine that the two objects have merged. If the system
determines that there have not been objects that have merged, then
the process continues at step 812 and the object history data
discussed above is updated so that all groups in the current motion
image have their center coordinates (x,y) used to update the
position of the objects being tracked. If, in step 810, the system
determines that the two objects have merged, then the objects are
separated by grouping the pixels based on proximity to the separate
objects in the previous motion images in step 814. In step 816, the
separated groups are assigned to the appropriate objects from the
previous motion image. In step 812, after step 816, the objects
history data is updated so that all groups in the current motion
image have their center coordinates (x,y) used to update the
position of the objects being tracked.
[0095] FIG. 13 is another embodiment of associating groups to
objects (step 468 of FIG. 8) that is based on predicting the
trajectory of moving objects using the object history data. In step
850 of FIG. 13, the system will access the object history data
discussed above. In step 852, using that data, the system can
predict the trajectory for each object being tracked. The system
knows the x and y coordinates in the motion images for previous
positions of the object. This data can be used to determine a
trajectory and predict where those objects should be in the current
motion image. In step 854, the system attempts to associate the
center of each of the groups in the current image with the
predicted trajectories. The system can also use the structure data
192 and gesture library 190 discussed above to predict the
trajectories. In step 858, any object that was not associated in
step 854 is assigned to be a new object in step 858. In step 860,
the system determines whether two objects have merged, as discussed
above. If no objects have merged, then the object history data is
updated in step 862 to include the information from the current
motion image. If two objects have merged (step 860), then in step
864 those groups are separated as discussed above with respect to
step 814. In step 866, the newly separated groups are assigned to
the previous objects (same as step 816). In step 862, the object
history data is updated, as discussed above.
[0096] Looking back at FIG. 8, after associating the groups with
objects being tracked the information about the objects is reported
to the application in step 470. In one example embodiment, steps
460-470 are performed by depth image processing and object
reporting module 194. In step 470, depth image processing and
object reporting module 194 reports to application 196 an
identification of all the objects it is tracking and the (X, Y)
positions of each of those objects in the current motion image. In
step 472, application 196 will update based on the reported object
information from step 470. The tracking of the objects can be
mapped directly to cursor control, where moving along the
perpendicular plane defines the two dimensional position of the
cursor and movement and depth can trigger other events. In such an
embodiment, step 470 would also include reporting depth (distance)
information for each object. For example, the depth values for all
the pixels in the group associated with the object can be averaged
and that data can be the depth number reported for the particular
object. Alternatively, all of the depth values can be reported to
the application or the depth value for the center of each group can
be reported.
[0097] In other embodiments, each object can correlate to an image
being displayed on a monitor as part of a video game or other
software application. When any of the objects move, application 196
will update the positions of the images on the monitor for the
object that moved. For example, if a person moves, the person's
avatar in the video game may move. If person throws the ball, an
image of the ball may move in the video game. There are many
different ways an application can update itself based on the motion
of the tracked objects. No particular way for updating the
application is required for the technology described herein.
[0098] The object history data may also incorporate information
about neighboring objects or the structure of a larger object
provided by an external system (e.g. structure data 192). For
example, if a moving object is identified to be the left arm of a
human body or a human head, the system can infer that a certain set
of pixels pertains to the left hand. Other variations can also be
implemented.
[0099] Although the subject matter has been described in language
specific to structural features and/or methodological acts, it is
to be understood that the subject matter defined in the appended
claims is not necessarily limited to the specific features or acts
described above. Rather, the specific features and acts described
above are disclosed as example forms of implementing the claims. It
is intended that the scope of the invention be defined by the
claims appended hereto.
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