U.S. patent application number 13/697212 was filed with the patent office on 2013-03-07 for detection of objects in an image using self similarities.
This patent application is currently assigned to TECHNISCHE UNIVERSITAT DARMSTADT. The applicant listed for this patent is Nikodem Majer, Gabriel Othmezouri, Ichiro Sakata, Bernt Schiele, Konrad Schindler, Stefan Walk. Invention is credited to Nikodem Majer, Gabriel Othmezouri, Ichiro Sakata, Bernt Schiele, Konrad Schindler, Stefan Walk.
Application Number | 20130058535 13/697212 |
Document ID | / |
Family ID | 42984005 |
Filed Date | 2013-03-07 |
United States Patent
Application |
20130058535 |
Kind Code |
A1 |
Othmezouri; Gabriel ; et
al. |
March 7, 2013 |
DETECTION OF OBJECTS IN AN IMAGE USING SELF SIMILARITIES
Abstract
An image processor (10) has a window selector for choosing a
detection window within the image, and a self similarity
computation part (40) for determining self-similarity information
for a group of the pixels in any part of the detection window, to
represent an amount of self-similarity of that group to other
groups in any other part of the detector window, and for repeating
the determination for groups in all parts of the detection window,
to generate a global self similarity descriptor for the detection
window. A classifier (50) is used for classifying whether an object
is present based on the global self-similarity descriptor. By using
global self-similarity rather than local similarities more
information is captured which can lead to better classification. In
particular, it helps enable recognition of more distant
self-similarities inherent in the object, and self-similarities
present at any scale.
Inventors: |
Othmezouri; Gabriel;
(Brussels, BE) ; Sakata; Ichiro; (Brussels,
BE) ; Schiele; Bernt; (Darmstadt, DE) ; Walk;
Stefan; (Darmstadt, DE) ; Majer; Nikodem;
(Reinheim, DE) ; Schindler; Konrad; (Darmstadt,
DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Othmezouri; Gabriel
Sakata; Ichiro
Schiele; Bernt
Walk; Stefan
Majer; Nikodem
Schindler; Konrad |
Brussels
Brussels
Darmstadt
Darmstadt
Reinheim
Darmstadt |
|
BE
BE
DE
DE
DE
DE |
|
|
Assignee: |
TECHNISCHE UNIVERSITAT
DARMSTADT
Darmstadt
DE
TOYOTA MOTOR EUROPE NV/SA
Brussels
BE
|
Family ID: |
42984005 |
Appl. No.: |
13/697212 |
Filed: |
February 28, 2011 |
PCT Filed: |
February 28, 2011 |
PCT NO: |
PCT/EP2011/052944 |
371 Date: |
November 9, 2012 |
Current U.S.
Class: |
382/103 |
Current CPC
Class: |
G06K 9/6215 20130101;
G06T 7/60 20130101; G06T 7/90 20170101; G06K 9/6267 20130101; G06K
9/4642 20130101; G06T 2207/20021 20130101; G06K 2009/4666 20130101;
G06K 9/52 20130101; G06K 9/00369 20130101; G06K 9/4652 20130101;
G06K 9/4647 20130101 |
Class at
Publication: |
382/103 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 11, 2010 |
EP |
10165769.0 |
Claims
1. An image processor (10) for detection of an object in an image
or sequence of images, each image being formed of pixels, and the
image processor comprising: a window selector for choosing a
detection window within the image, a self similarity computation
part (40) for determining self-similarity information for a group
of the pixels in any part of the detection window, to represent an
amount of self-similarity of that group to other groups in any
other part of the detector window, and for repeating the
determination for groups in all parts of the detection window, to
generate a global self similarity descriptor for the chosen
detection window, and a classifier (50) for classifying whether the
object is present in the detection window of the image from the
global self-similarity descriptor for that detection window.
2. The image processor of claim 1, the self similarity information
comprising an amount of self-similarity of colours of pixels of the
group.
3. The image processor of claim 1, having a part (42) arranged to
determine a distribution of colours of the pixels of the groups,
and the self similarity information comprising an amount of
self-similarity of the colour distributions.
4. The image processor of claim 1, having a part (30) for
determining gradient information by determining a distribution of
intensity gradients in a cell of pixels, and inputting such
gradient information for cells over all parts of the detection
window to the classifier, the classifier additionally being
arranged to use the gradient information to classify whether the
object is present.
5. The image processor of claim 1, having a part arranged to
determine a flow of the groups in terms of motion vectors of the
pixels of the groups over successive images in a sequence of
images, and the self-similarity information comprising an amount of
self-similarity of the flow.
6. The image processor of claim 1, the self-similarity computation
part having a histogram generator (44) arranged to determine a
histogram of values for a feature of pixels in the group, by using
interpolation.
7. The image processor of claim 6, the self similarity computation
part having a part (46) arranged to determine similarities between
histograms for different groups of pixels in the detection window
by a histogram intersection.
8. The image processor of claim 1, comprising a motion detection
part (70) for detecting motion vectors for parts of the image, and
the classifier part being arranged to classify based also on the
motion vectors of parts in the detection window.
9. The image processor of claim 1, having a combiner part (60) for
combining the similarity information and the distributions of
intensity gradients before input to the classifier.
10. A method of using an image processor for detection of an object
in an image or sequence of images, each image being formed of
pixels, and the method having the steps of: choosing (100, 130) a
detection window within the image, using the image processor to
determine (110) self-similarity information for a group of the
pixels in any part of the detection window, to represent an amount
of self-similarity of that group to other groups in any other part
of the detector window, repeating the determination (130) for
groups in all parts of the detection window, to generate a global
self similarity descriptor for the chosen detection window, and
classifying (140) whether the object is present in the detection
window of the image from the global self-similarity descriptor for
that detection window.
11. The method of claim 10, having the step of determining a
distribution of colours of the pixels of the groups, and the self
similarity information comprising an amount of self-similarity of
the colour distributions.
12. The method of claim 10, having the steps of determining
gradient information by determining a distribution of intensity
gradients in a cell of pixels, and determining such gradient
information for cells over all parts of the detection window, and
the classifying step additionally using the gradient information to
classify whether the object is present.
13. The method of claim 10, having the step of determining a flow
of the groups in terms of motion vectors of the pixels of the
groups over successive images in a sequence of images, and the
self-similarity information comprising an amount of self-similarity
of the flow.
14. A program on a computer readable medium and having instructions
which when executed by a computer cause the computer to carry out
the method of claim 10.
15. An integrated circuit having the image processor of claim 1.
Description
FIELD OF THE INVENTION
[0001] This invention relates to apparatus and methods for image
processing to detect objects such as humans, and to corresponding
computer programs for carrying out such methods and to memory
devices storing the computer programs and also to corresponding
integrated circuits.
BACKGROUND OF THE INVENTION
[0002] Pedestrian detection has been a focus of recent research due
to its importance for practical applications such as automotive
safety [see refs 11, 8] and visual surveillance [23]. The most
successful model to date for "normal" pedestrians, who are usually
standing or walking upright, is still a monolithic global
descriptor for the entire search window. With such a model, there
are three main steps which can be varied to gain performance:
feature extraction, classification, and non-maxima suppression. The
most common features extracted from the raw image data are variants
of the HOG framework, i.e. local histograms of gradients and
(relative) optic flow [3, 4, 10, 24, 27], and different flavors of
generalized Haar wavelets, e.g. [6, 23]. Competitive classifiers we
know of employ statistical learning techniques to learn the mapping
from features to scores (indicating the likelihood of a pedestrian
being present)--usually either support vector machines [3, 13, 17,
19, 27] or some variant of boosting [23, 27, 28, 30].
[0003] The spectacular progress that has been made in detecting
pedestrians (i.e. humans in an upright position) is maybe best
illustrated by the increasing difficulty of datasets used for
benchmarking The first [16] and second [3] generation of pedestrian
databases are essentially saturated, and have been replaced by new
more challenging datasets [7, 27, 6]. These recent efforts to
record data of realistic complexity have also shown that there is
still a gap between what is possible with pedestrian detectors and
what would be required for many applications: in [6] the detection
rate of the best methods is still <60% for one false positive
detection per image, even for fully visible people.
SUMMARY OF THE INVENTION:
[0004] An object of the invention is to provide apparatus and
methods for image processing to detect objects such as humans, and
to corresponding computer programs for carrying out such methods
and to corresponding integrated circuits. According to a first
aspect, the invention provides:
[0005] An image processor for detection of an object in an image or
sequence of images, each image being formed of pixels, and the
image processor comprising: a window selector for choosing a
detection window within the image, a self similarity computation
part for determining self-similarity information for a group of the
pixels in any part of the detection window, to represent an amount
of self-similarity of that group to other groups in any other part
of the detector window, and for repeating the determination for
groups in all parts of the detection window, to generate a global
self similarity descriptor for the chosen detection window, and a
classifier for classifying whether the object is present in the
detection window of the image from the global self-similarity
descriptor for that detection window.
[0006] By using global self-similarity rather than local
similarities more information is captured which can lead to better
classification. In particular, it helps enable recognition of more
distant self-similarities inherent in the object, and
self-similarities present at any scale. The classifier can then
make use of or capture those self similarities which are most
discriminant of the object.
[0007] Embodiments of the invention can have any other features
added, some such additional features are set out in dependent
claims and described in more detail below.
[0008] Other aspects of the invention include corresponding
methods, and computer programs. Any of the additional features can
be combined together and combined with any of the aspects, or can
be disclaimed. Other advantages will be apparent to those skilled
in the art, especially over other prior art. Numerous variations
and modifications can be made without departing from the claims of
the present invention. Therefore, it should be clearly understood
that the form of the present invention is illustrative only and is
not intended to limit the scope of the claims of the present
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] How the present invention may be put into effect will now be
described by way of example with reference to the appended
drawings, in which:
[0010] FIG. 1 shows an image processor according to a first
embodiment,
[0011] FIG. 2 shows method steps according to an embodiment,
[0012] FIG. 3 shows another embodiment,
[0013] FIG. 4 shows views of a window of an image and self
similarity information from four cells in different parts of the
image,
[0014] FIG. 5 shows an image processor according to an embodiment
having a motion detector,
[0015] FIG. 6 shows steps in determining gradient information
according to an embodiment,
[0016] FIG. 7 shows steps in determining self similarity
information according to an embodiment,
[0017] FIG. 8 shows an example of an image showing detection
windows, and
[0018] FIG. 9 shows steps according to another embodiment.
DETAILED DESCRIPTION
[0019] The present invention will be described with respect to
particular embodiments and with reference to certain drawings but
the invention is not limited thereto but only by the claims. The
drawings described are only schematic and are non-limiting. In the
drawings, the size of some of the elements may be exaggerated and
not drawn on scale for illustrative purposes. Where the term
"comprising" is used in the present description and claims, it does
not exclude other elements or steps. Where an indefinite or
definite article is used when referring to a singular noun e.g. "a"
or "an", "the", this includes a plural of that noun unless
something else is specifically stated.
[0020] The term "comprising", used in the claims, should not be
interpreted as being restricted to the means listed thereafter; it
does not exclude other elements or steps. Thus, the scope of the
expression "a device comprising means A and B" should not be
limited to devices consisting only of components A and B. It means
that with respect to the present invention, the only relevant
components of the device are A and B.
[0021] Furthermore, the terms first, second, third and the like in
the description and in the claims, are used for distinguishing
between similar elements and not necessarily for describing a
sequential or chronological order. It is to be understood that the
terms so used are interchangeable under appropriate circumstances
and that the embodiments of the invention described herein are
capable of operation in other sequences than described or
illustrated herein.
[0022] Moreover, the terms top, bottom, over, under and the like in
the description and the claims are used for descriptive purposes
and not necessarily for describing relative positions. It is to be
understood that the terms so used are interchangeable under
appropriate circumstances and that the embodiments of the invention
described herein are capable of operation in other orientations
than described or illustrated herein.
[0023] Reference throughout this specification to "one embodiment"
or "an embodiment" means that a particular feature, structure or
characteristic described in connection with the embodiment is
included in at least one embodiment of the present invention. Thus,
appearances of the phrases "in one embodiment" or "in an
embodiment" in various places throughout this specification are not
necessarily all referring to the same embodiment, but may.
Furthermore, the particular features, structures or characteristics
may be combined in any suitable manner, as would be apparent to one
of ordinary skill in the art from this disclosure, in one or more
embodiments.
[0024] Similarly it should be appreciated that in the description
of exemplary embodiments of the invention, various features of the
invention are sometimes grouped together in a single embodiment,
figure, or description thereof for the purpose of streamlining the
disclosure and aiding in the understanding of one or more of the
various inventive aspects. This method of disclosure, however, is
not to be interpreted as reflecting an intention that the claimed
invention requires more features than are expressly recited in each
claim. Rather, as the following claims reflect, inventive aspects
lie in less than all features of a single foregoing disclosed
embodiment. Thus, the claims following the detailed description are
hereby expressly incorporated into this detailed description, with
each claim standing on its own as a separate embodiment of this
invention.
[0025] Furthermore, while some embodiments described herein include
some but not other features included in other embodiments,
combinations of features of different embodiments are meant to be
within the scope of the invention, and form different embodiments,
as would be understood by those in the art. For example, in the
following claims, any of the claimed embodiments can be used in any
combination.
[0026] Furthermore, some of the embodiments are described herein as
a method or combination of elements of a method that can be
implemented by a processor of a computer system or by other means
of carrying out the function. Thus, a processor with the necessary
instructions for carrying out such a method or element of a method
forms a means for carrying out the method or element of a method.
Furthermore, an element described herein of an apparatus embodiment
is an example of a means for carrying out the function performed by
the element for the purpose of carrying out the invention.
References to a signal can encompass any kind of signal in any
medium, and so can encompass an electrical or optical or wireless
signal or other signal for example. References to analyzing can
encompass processing a signal in any way to derive or enhance
information about the material. References to a processor can
encompass any means for processing signals or data in any form and
so can encompass for example a personal computer, a microprocessor,
analog circuitry, application specific integrated circuits,
software for the same, and so on.
[0027] In the description provided herein, numerous specific
details are set forth. However, it is understood that embodiments
of the invention may be practiced without these specific details.
In other instances, well-known methods, structures and techniques
have not been shown in detail in order not to obscure an
understanding of this description.
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Introduction to some Issues Addressed by some of the
Embodiments
[0058] At least some embodiments of the present invention have a
new feature based on self-similarity of low level features, in some
particular embodiments color histograms from different sub-regions
within the detector window. This feature, termed CSS, captures
pairwise statistics of spatially localized color distributions,
thus being independent of the actual color of a specific example.
It is global similarities which are found, in the sense of
similarities of a given sub-region with any other part of the
detection window, not just the parts adjacent to the sub-region.
The self-similarity enables representation of properties like "the
color distributions on the left and right shoulder usually exhibit
high similarity", independently of the actual color distribution,
which may vary from person to person depending on their clothing.
Adding CSS can improve state-of-the-art classification performance
for both static images and image sequences. The new feature is
particularly powerful for static images, and hence also valuable
for applications such as content-based image retrieval. It also
yields a consistent improvement on image sequences, in combination
with motion detection in the form of optic flow.
[0059] Instance-specific color information was recently used in the
form of implicit local segmentation features [15], encoding
gradients of distances w.r.t. two local color distribution models
("foreground" and "background"). Only few authors have advocated
the use of self-similarity as a feature.
[0060] Most notably, [20] encodes the local self-similarity of raw
image patches in a log-polar binned descriptor. They demonstrate
superior performance over gradient features in a template matching
task, which can include matching particular human poses. In [12]
the authors propose self-similarity descriptors over feature time
series for human action recognition, observing good viewpoint
invariance of the descriptor.
[0061] In a different context, [21] proposed a representation where
color similarity is computed at the pixel level, assuming a
Gaussian conditional color distribution.
[0062] Usually the choice of features is the most critical decision
when designing a detector, and finding good features is still
largely an empirical process with few theoretical guidelines.
Different combinations of features were evaluated, including a new
feature based on the similarity of colors in different regions of
the detector window, which can significantly raise detection
performance. The pedestrian region in one embodiment of our
detection window is of size 48.times.96 pixels. As it has been
shown to be beneficial to include some context around the person
[3] the window itself is larger (64.times.128 pixels). HOG
Histograms of oriented gradients are a known feature for object
detection, first proposed in [3]. They collect gradient information
in local cells into histograms using trilinear interpolation, and
normalize overlapping blocks composed of neighbouring cells.
Interpolation, local normalization and histogram binning make the
representation robust to changes in lighting conditions and small
variations in pose. HOG can optionally be enhanced by Local Binary
Patterns (LBP) [24].
FIGS. 1, 2 a First Embodiment
[0063] FIG. 1 shows an image processor according to an embodiment.
FIG. 2 shows steps carried out by this or other embodiments. The
image processor can be implemented as for example one or more
integrated circuits having hardware such as circuit blocks
dedicated to each of the parts shown, or can be implemented for
example as software modules executed by a general purpose processor
in sequence, as in a server. The parts shown include a selector 20
for receiving an input image or image stream (such as frames of a
video, in real time or non real time) from an image source device
5, and selecting a detection window, and within that window,
selecting groups of pixels to be processed. The groups can be e.g.
6.times.6 or 8.times.8 pixels or different sizes. They need not be
square, and can be rectangular or other regular or irregular shape.
Groups are processed by a global self similarity computation part
40. The self similarity computation part determines self similarity
information for a group of the pixels in any part of the detection
window, to represent an amount of self-similarity of that group to
other groups in any other part of the detector window, and repeats
the determination for groups in all parts of the detection window,
to generate a global self similarity descriptor for the chosen
detection window. Again this can be implemented in various ways,
and an example will be described below in more detail with
reference to FIG. 7.
[0064] The self similarity information for different parts of the
window can be determined in parallel or sequentially and are fed to
a classifier 50. This determines if the information corresponds to
the object being sought. This can be a binary decision or can
produce a score, and can be carried out in various ways, and an
example is described in more detail below. There can be other parts
to the image processor not shown in this figure, at any stage of
the processor. A device 55 can be provided for interpreting or
taking action based on the classifier score or decision. This can
be for example a vehicle control system, or driver assistance
system, a robotic system, a surveillance system for detecting
intruders and so on.
[0065] FIG. 2 shows steps in operating the image processor of FIG.
1 or of other embodiments. At step 100, a window is selected, and a
first group of pixels is selected. At step 110, self similarity
information is determined for a group of the pixels in any part of
the detection window, to represent an amount of self-similarity of
that group to other groups in any other part of the detector
window. At step 120, this is repeated for a next group, if the
global self-similarities have not been processed for all parts of
the window. Once it has been done for all parts of the detection
window, to generate a global self similarity descriptor for the
chosen detection window, this descriptor can be used by the
classifier. At step 140, the classification of whether an object is
present in the image is made from the descriptor.
Additional Features of some Embodiments
[0066] Additional features can include the self similarity
information comprising an amount of self-similarity of colours of
pixels of the group. This is one of several useful features which
can help distinguish objects such as humans in particular. The
image processor can have a part (42) arranged to determine a
distribution of colours of the pixels of the groups, and the self
similarity information comprising an amount of self-similarity of
the colour distributions. This is another feature which can help
distinguish objects such as humans in particular.
[0067] In some cases there is provided a part (30) for determining
gradient information by determining a distribution of intensity
gradients in a cell of pixels, and for inputting such gradient
information for cells over all parts of the detection window to the
classifier, the classifier additionally being arranged to use the
gradient information to classify whether the object is present. The
gradient information can be complementary to the self similarity
information in many cases, and hence provide more distinctive
information to the classifier to help enable better
classification.
[0068] The image processor can have a part arranged to determine a
flow of the groups in terms of motion vectors of the pixels of the
groups over successive images in a sequence of images, and the
self-similarity information comprising an amount of self-similarity
of the flow. This is another feature which can help distinguish
moving objects such as pedestrians.
[0069] The self-similarity computation part can have a histogram
generator (44) arranged to determine a histogram of values for a
feature of pixels in the group, by using interpolation. Such
interpolation enables some data compression, to reduce computation
load for subsequent steps, and enable faster or cheaper
processing.
[0070] The self similarity computation part can have a part (46)
arranged to determine similarities between histograms for different
groups of pixels in the detection window by a histogram
intersection. Histogram intersection is one of a number of ways of
determining similarities and proves to be particularly efficient
and effective.
[0071] The image processor can comprise a motion detection part
(70) for detecting motion vectors for parts of the image, and the
classifier part being arranged to classify based also on the motion
vectors of parts in the detection window. Such motion information
is also useful to distinguish humans in some situations and is
often complementary to the self similarity information.
[0072] The image processor can have a combiner part (60) for
combining the similarity information and the distributions of
intensity gradients before input to the classifier. Although in
principle the classifier could operate on the information
separately, it is usually more efficient to combine the information
first.
FIG. 3, Embodiment of Global Self Similarity Computation Part
[0073] FIG. 3 shows an embodiment similar to that of FIG. 1, but
showing more details of one way to implement part 40, for computing
the global self-similarity information, showing some of the
functions it can carry out. Other ways can be envisaged. In this
case, the feature for the self similarity information is colour
distribution, so there is shown a step of determining colour
distribution for a group of pixels 42. At step 44 a histogram is
generated, optionally using interpolation 44 to reduce the amount
of data and reduce aliasing effects. The histograms for different
groups are typically stored and retrieved as needed by a step 46 of
determining the similarity between the histogram of a given group
and other groups anywhere in the detection window. If all
histograms are compared to all others, then the result can be a
large number of similarity values, which can be regarded as a
multidimensional matrix or vector, having as many dimensions as
there are pairs of groups (that is G*(G-1)/2) where G is the number
of groups. The groups can be adjacent or overlapping, or spread
apart, but should be chosen from different parts of the window so
that the self-similarities are global within the window, and not
local in the sense of being only relative to other groups adjacent
to the group being considered. This can lead to a normalizing step
48 for normalizing the output vector to account for conditions such
as camera noise/image artifacts/different amounts of clutter, or
any other causes of some images having overall a lower
"self-similarity" for example. A combiner 60 can be provided for
combining the self-similarity information with other information
such as gradient information. This implies there is a compatible
data format for both, so the gradient information can be in the
form of a vector which can be stacked with the vector of self
similarity information, for input to the classifier.
[0074] Then the combined data can be fed to the classifier 50 for
the decision or scoring of whether the object has been detected.
This can be repeated for other detection windows within the image.
The other windows can be chosen for example by sliding, by zooming
to alter the scale, or by seeking areas of interest, using known
algorithms which need not be described here.
FIG. 4 Views of Self Similarity Information
[0075] FIG. 4 shows an example detection window within an image on
the left hand side and shows on its right, four different views of
self similarity information determined for four particular points
in this example. The self similarity information is computed at
marked cell positions using HSV+histogram intersection methods as
discussed in more detail below. Cells with higher similarity are
brighter. Of the four self similarity views, the first on the left
represents a colour similarity of every other group of pixels with
a group located at the head of the human in the image. The second
view is the colour similarity of every other group of pixels with a
group located at the stomach of the human in the image. The third
view is the colour similarity of every other group of pixels with a
group located at an upper leg region of the human in the image. The
fourth view is the colour similarity of every other group of pixels
with a group located off the human, at a point showing water in the
background in the image. Note how self-similarity highlights and
distinguishes relevant parts like clothing and visible skin
regions.
FIG. 5, Embodiment with Motion Detection
[0076] FIG. 5 shows an embodiment similar to that of FIG. 1. A
window/cell/group selector part 21 selects a detection window
within an image, and within that window, selects groups or cells of
pixels. The window selection can be by sliding, scaling, or finding
an area of interest for example. The cells and groups can be the
same size, e.g. 6.times.6 or 8.times.8 pixels or different sizes.
They need not be square, and can be rectangular or other regular or
irregular shape. Cells are fed to the gradient computation part 30.
Groups are sent to the self similarity computation part 40. Groups
and cells are so named to show that different pixels can be sent to
different parts for processing, though in principle the same cells
could be sent to both parts shown.
[0077] The gradient computation part determines gradient
information such as a steepness of the gradient of intensity, and
an orientation for that cell. This can be intensity of brightness
or intensity of colours for example. Various algorithms can be used
to implement this part, an example is described in more detail
below. The global self similarity computation part determines self
similarity information for the group, relative to any other parts
of the window. Again this can be implemented in various ways, and
an example will be described below in more detail with reference to
FIG. 7.
[0078] The gradient information and the self similarity information
can be determined in parallel or sequentially and are both fed to a
classifier 50.
[0079] In this case a motion detector part 70 can be added, which
can determine motion information such as optic flow for a given
cell or group, based on frames (preferably consecutive frames).
This can be implemented in various ways following established
practice, and so will not be described in more detail here. A
possible enhancement to this part is described below.
FIG. 6, Steps in Determining Gradient Information
[0080] In FIG. 6, steps are shown for one way to implement the
gradient computation part 30 for determining gradient information
in the form of distribution of intensity gradients in each cell. At
step 200, a window is divided into cells of 8.times.8 pixels. At
step 210, a gradient value for that cell is determined and the
orientation of the gradient is determined, from the 64 pixel
values, of intensity or colour values as appropriate. At step 220,
these values are separated into 9 bins, one for each of the
different orientations. At step 230, these steps are repeated for
other cells, and the bins for different cells are grouped into
2.times.2 blocks of cells, overlapping by one cell for example.
Normalization at step 250 is carried out on a block basis. Other
ways of implementation can be envisaged.
FIG. 7, Steps in Determining Global Self Similarity Information
[0081] In FIG. 7 steps are shown for one way to implement the step
110 of determining global self similarity information. At step 300,
the window is divided into 8.times.8 groups of pixels. At step 310,
trilinear interpolation is used to compute 128 local colour
histograms from 128 8.times.8 groups of pixels, and/or flow
histograms as appropriate. Colour is represented in the well known
HSV format, though other colour representations could be used. Flow
can be represented as motion vectors. At step 320, pairs of these
histograms are compared to determine a value for similarity. The
histogram intersection method is used though other methods could be
used. If all 128 are compared to all others, this results in a 8128
dimensional vector of similarity values. At step 330, L2
normalization is applied to this vector. Results in the form of
normalized vectors are output to the combiner or classifier.
FIG. 8 Example of Image Showing Detection Windows
[0082] This figure shows an example of an image containing many
objects, some of which are humans, and some are overlapping with
others giving rise to occlusions. Detection windows around each
possible human are shown. These may be selected based on sliding a
window over the image and comparing scores of different windows,
and windows at different scales.
FIG. 9, Steps in Another Embodiment
[0083] In FIG. 9, at step 400, a window is moved over an image, by
sliding, scaling or seeking an area of interest. At step 410, a
global self-similarity descriptor and optionally other features are
obtained for that window. At step 420 a classifier is used to
generate a score or a decision for each window. At step 430, scores
for different windows are compared, and this may be used to decide
where to move and/or scale the window. This may lead to repeating
steps 400 to 430. At any time, step 440 may be carried out, using
the scores for different windows to locate an object and take
action based on the scores and the location, such as to control a
vehicle or robot, or raise an alarm in a surveillance system for
example.
Practical Considerations for HOG/HOF
[0084] In experiments histograms were computed with 9 bins on cells
of 8.times.8 pixels. Blocksize was 2.times.2 cells overlapping by
one cellsize. HOF Histograms of flow were initially also proposed
by Dalal et al. [4]. We determined that using them (e.g. in [4]'s
IMHwd scheme) complementary to HOG can give substantial
improvements on realistic datasets with significant motion of the
humans. In some embodiments of the present invention a
lower-dimensional variant of HOF, IMHd2 is introduced. This encodes
motion differences within 2.times.2 blocks with 4 histograms per
block, while matching the performance of IMHwd (3.times.3 blocks
with 9 histograms). The new coding scheme can be explained as
follows:
[0085] The 4 squares display the encoding for one histogram each.
For the first histogram, the optical flow corresponding to the
pixel at the ith row and jth column of the upper left cell is
subtracted from the one at the corresponding position of the lower
left cell, and the resulting vector votes into a histogram as in
the original HOF scheme. IMHd2 provides a dimensionality reduction
of 44% (2520 instead of 4536 values per window), without changing
performance significantly.
[0086] We used the publicly available flow implementation of [26].
HOF continues to provide a substantial improvement even for flow
fields computed on JPEG images with strong block artifacts (and
hence degraded flow fields).
[0087] Several authors have reported improvements by combining
multiple types of low-level features [5, 18, 27]. Still, it is
largely unclear which cues could be used in addition to the known
combination of gradients and optic flow, as there are many
different aspects to the image statistics. Color information is
such a feature enjoying popularity in image classification [22] but
is nevertheless rarely used in detection. Furthermore, second order
image statistics, especially co-occurrence histograms, are gaining
popularity, pushing feature spaces to extremely high dimensions
[25, 18].
CSS
[0088] Embodiments of the present invention can combine two of
these ideas and use second order statistics of colors for example
as an additional feature. Color by itself is of limited use,
because colors vary across the entire spectrum both for people
(respectively their clothing) and for the background, and because
of the essentially unsolved color constancy problem. However,
people do exhibit some structure, in that colors are locally
similar--for example (see FIG. 4) the skin color of a specific
person is similar on their two arms and face, and the same is true
for most people's clothing. Therefore, we encode color
self-similarities within the descriptor window, i.e. similarities
between colors in different sub-regions. To leverage the robustness
of local histograms, we compute D local color histograms over
8.times.8 pixel blocks, using trilinear interpolation as in HOG to
minimize aliasing. We experimented with different color spaces,
including 3.times.3.times.3 histograms in RGB, HSV, HLS and CIE Luv
space, and 4.times.4 histograms in normalized rg, HS and uv,
discarding the intensity and only keeping the chrominance. Among
these, HSV worked best, and is used in the following.
[0089] The histograms form the base features between which pairwise
similarities are computed. Again there are many possibilities to
define similarity between histograms. We experimented with a number
of well-known distance functions including the L1-norm, L2-norm,
X.sup.2-distance, and histogram intersection. We used histogram
intersection as it worked best. Finally, we applied
L2-normalization to the (D.(D-1)/2)-dimensional vector of
similarities. In our implementation with D=128 blocks, CSS has 8128
dimensions. Normalization proved to have a considerable effect in
combination with SVM classifiers. Note that CSS circumvents the
color-constancy problem by only comparing colors locally. In
computation cost, CSS is on the same order of magnitude as HOF.
[0090] Self-similarity of colors is more appropriate than using the
underlying color histograms directly as features. CSS in HSV space
yields a noticeable improvement. On the contrary adding the color
histogram values directly even hurts the performance of HOG. In an
ideal world this behavior should not occur, since SVM training
would discard un-informative features. Unfortunately this holds
only if the feature statistics are identical in the training and
test sets. In our setup--and in fact quite often in practice--this
is not the case: the training data was recorded with a different
camera and in different lighting conditions than the test data, so
that the weights learned for color do not generalize from one to
the other. A similar observation was made by [27], in which the
author found that adding Haar features can sometimes help, but
careful normalization is required, if the imaging conditions vary.
Note that [5] shows successfully utilizing (raw) color, and so
embodiments can be envisaged in which it is incorporated as a
factor in the classifier of the detector (e.g. skin color may in
principle be a sensible cue).
[0091] Note that self-similarity is not limited to color histograms
and directly generalizes to arbitrary localized subfeatures within
the detector window. We experimented with self-similarity on
features such as gradient orientation in the form of HOG blocks or
motion detection features such as flow histograms.
Classifiers
[0092] Linear SVMs remain a popular choice for people detection
because of their good performance and speed. Nonlinear kernels
typically bring some improvement, but commonly the time required to
classify an example is linear in the number of support vectors,
which is intractable in practice. An exception is the (histogram)
intersection kernel (HIK) [14], which can be computed exactly in
logarithmic time, or approximately in constant time, while
consistently outperforming the linear kernel.
[0093] Viola et al. [23] used AdaBoost in their work on pedestrian
detection. However, it has since been shown that AdaBoost does not
perform well on challenging datasets with multiple viewpoints [27].
MPLBoost remedies some of the problems by learning multiple
(strong) classifiers in parallel. The final score is then the
maximum score over all classifiers, allowing individual classifiers
to focus on specific regions of the feature space without degrading
the overall classification performance.
Discussion of Results
[0094] Results obtained with different variants of our detector
will now be discussed. On Caltech Pedestrians, we used the
evaluation script provided with the dataset. For TUD-Brussels we
evaluated on the full image, including pedestrians at the image
borders (in contrast to [27]), who are particularly important for
practical applications--e.g. for automotive safety, near people in
the visual periphery are the most critical ones. Unless noted
otherwise, the classifier used with our detector is HIKSVM.
[0095] Performance was measured on the "reasonable" subset of
Caltech Pedestrians, which is the most popular portion of the data.
It consists of pedestrians of .gtoreq.50 pixels in height, who are
fully visible or less than 35% occluded. Our detector in its
strongest incarnation, using HOG, HOF and CSS in a HIKSVM
(HOGF+CSS), outperforms the previous top performers--the channel
features (ChnFtrs) of [5] and the latent SVM (LatSvm-V2) of
[10]--by a large margin:
[0096] 10.9% at 0.01 fppi, 14.7% at 0.1 fppi and 7.0% at 1 fppi. We
also note that our baseline, HOG with HIKSVM, is on par with the
state of the art [5, 10], which illustrates the effect of correct
bootstrapping, and the importance of careful implementation. We did
not tune our detector to the dataset. Still, to make sure the
performance gain is not dataset-specific, we have verified that our
detector outperforms the original HOG implementation [3] also on
INRIAPerson (also note that adding CSS provides an improvement for
HOG+LBP). HOG+CSS is consistently better than HOG alone, providing
an improvement of 5.9% at 0.1 fppi, which indicates that color
self-similarity is indeed complementary to gradient information.
HOG+HOF improves even more over HOG, especially for low false
positive rates: at 0.1 fppi the improvement is 10.9%. This confirms
previous results on the power of motion as a detection cue.
Finally, HOG+HOF+CSS is better than only HOG+HOF, showing that CSS
also contains information complementary to the flow, and achieves
our best result of 44.35% recall at 0.1 fppi.
[0097] The performance on the "near" subset (80 pixels or taller)
showed that again, our baseline (HOG(our)) is at least on par with
the state of the art [5, 10]. HOG+CSS provided better performance
between 0.01 and 0.5 fppi, 6% at 0.1 fppi. Adding HOF to HOG (HOGF)
added 19.9% recall at 0.01 fppi. At 0.1 fppi it beat the closest
competitor HOG+CSS by 11% and the best published result (LatSvm-V2)
by 21.2%. Adding CSS brought another small improvement for large
pedestrians. The reason that HOF works so well on the "near" scale
is probably that during multiscale flow estimation compression
artifacts are less visible at higher pyramid levels, so that the
flow field is more accurate for larger people.
[0098] Evaluation was also carried out for increasing occlusion
levels. Results for the "no occlusion" subset, were almost
identical to a subset where only approximately 5% of the
"reasonable" pedestrians are partially occluded. Plots are also
stretched vertically to provide for better readability. Evaluated
on the partially occluded pedestrians alone (which is not a
significant statistic, because there are only about 100 such
examples), latent SVM and channel features slightly outperform our
HOG, but again are dominated by HOG+HOF, with CSS again bringing a
further small improvement. On the heavily occluded pedestrians the
performance of all evaluated algorithms is abysmal. A lack of
robustness to heavy occlusion is a well-known issue for global
detectors. Still, there is a noticeable relative improvement with
our detector:
[0099] At 0.1 fppi, the recall of HOG+HOF+CSS is at 7.8% compared
to 3.9% for ChnFtrs, doubling the recall. At 1 fppi, our full
detector still performs best, with 5.9% higher recall than
LatSvm-V2. That colour self-similarity helps in the presence of
occlusion may seem counter-intuitive at first, because occlusion of
a local sub-region is likely to affect its similarity to all other
sub-regions. However, in the case of Caltech, "heavy occlusion"
mostly means that the lower part of the body is occluded, so that
similarities between different parts of the upper body can still be
used.
[0100] An improvement was gained by adding CSS on the TUD-Brussels
dataset. CSS adds little in the high precision regime, but starting
at 0.05 fppi there is a notable boost in performance, as recall is
improved by 2.7% at 0.1 fppi and 4.2% at 1 fppi. For static images
with no flow information, the improvement starts earlier, reaching
3.6% at 0.1 fppi and 5.4% at 1 fppi.
[0101] If the results of [27] on TUDBrussels are compared, in this
paper Haar features did provide an improvement only on that
dataset, on others they often cost performance. This is in contrast
to CSS, which so far have produced consistent improvements, even on
datasets with very different image quality and colour statistics.
Judging from the available research, Haar features can potentially
harm more than they help.
[0102] For the static image setting, HOG+CSS consistently
outperformed the results of [27] by 5%-8% against HOG+Haar with
MPLBoost, and by 7%-8% against HOG with HIKSVM. Utilizing motion,
the detector of [27] using HOG+HOF (in the IMHwd scheme), Haar
features and a linear SVM is on par with HOG+HOF+CSS for low false
positive rates, but it starts to fall back at 0.2 fppi. The result
of [27] using HOG+HOF with HIKSVM is consistently worse by 3%-5%
than HOG+HOF+CSS, especially at low false positive rates. We have
in all cases used the tools and detections used in the original
publications [6, 27] for the respective datasets.
[0103] One evaluation was on the "far" subset of the Caltech
dataset. In this setting, only pedestrians with an annotated height
20 to 30 pixels were considered. Detections fulfilling the Pascal
condition can be as small as 10 pixels or as large as 59 pixels.
Any annotation inside the 20-30 pixel range can be matched by a
detection outside the range. This introduces an asymmetry which is
difficult to handle. The Caltech evaluation script discards all
detections outside the considered range, resulting in situations
where a pedestrian with an annotated height of 29 pixels and a
detected height of 30 pixels counts as a missed detection, although
VU>90%. This is clearly undesirable, especially if many
annotations are close to the size limit (which is always the case
for small size ranges). However, trying to fix this bias introduces
other ones. One possibility is to establish correspondence with the
full sets of annotation and detection, and prune for size
afterwards.
Computer Implementations
[0104] Some of the method steps discussed above for determining a
distribution density or determining self-similarity information, or
detecting a human in the image for example, may be implemented by
logic in the form of hardware or, for example, in software using a
processing engine such as a microprocessor or a programmable logic
device (PLD's) such as a PLA (programmable logic array), PAL
(programmable array logic), FPGA (field programmable gate
array).
[0105] An example of a circuit with an embedded processor will be
described for use in applications such as vehicle control or driver
assistance or monitoring of surveillance cameras. This circuit may
be constructed as a VLSI chip around an embedded microprocessor
such as an ARM7TDMI core designed by ARM Ltd., UK which may be
synthesized onto a single chip with the other components shown.
Alternatively other suitable processors may be used and these need
not be embedded, e.g. a Pentium processor as supplied by Intel
Corp. USA. A zero wait state SRAM memory may be provided on-chip as
well as a cache memory for example. Typically I/O (input/output)
interfaces are provided for receiving and transmitting data to
relevant networks, e.g. wireless or cable networks. FIFO buffers
may be used to decouple the processor from data transfer through
these interfaces. The interface can provide network connections,
i.e. suitable ports and network addresses, e.g. the interfaces may
be in the form of network cards.
[0106] Software programs may be stored in an internal ROM (read
only memory) and/or on any other non-volatile memory, e.g. they may
be stored in an external memory. Access to an external memory may
be provided an external bus interface if needed, with address, data
and control busses. The method and apparatus of the embodiments
described may be implemented as software to run on a processor. In
particular an image processor in accordance with the present
invention may be implemented by suitable programming of a
processor. The methods and procedures described above may be
written as computer programs in a suitable computer language such
as C and then compiled for the specific processor in the embedded
design. For example, for the embedded ARM core VLSI described above
the software may be written in C and then compiled using the ARM C
compiler and the ARM assembler. The software has code, which when
executed on a processing engine provides the methods and the
apparatus of the present invention. The software programs may be
stored on any suitable machine readable medium such as magnetic
disks, diskettes, solid state memory, tape memory, optical disks
such as CD-ROM or DVD-ROM, etc.
[0107] In conclusion, as described, an image processor (10) has a
window selector for choosing a detection window within the image,
and a self similarity computation part (40) for determining
self-similarity information for a group of the pixels in any part
of the detection window, to represent an amount of self-similarity
of that group to other groups in any other part of the detector
window, and for repeating the determination for groups in all parts
of the detection window, to generate a global self similarity
descriptor for the detection window. A classifier (50) is used for
classifying whether an object is present based on the global
self-similarity descriptor. By using global self-similarity rather
than local similarities more information is captured which can lead
to better classification. In particular, it helps enable
recognition of more distant self-similarities inherent in the
object, and self-similarities present at any scale. Other
variations can be envisaged within the scope of the claims.
* * * * *