Proceedings of the IEEE ITSC 2006
2006 IEEE Intelligent Transportation Systems Conference
Toronto, Canada, September 17-20, 2006
MC4.2
A Low-cost Occlusion Handling Using a Novel Feature
In Congested Traffic Images
M. Sadeghi and M. Fathy
Abstract—Occlusion detection is critical for robust and
reliable vision-based systems for traffic flow analysis. In this
paper we discuss novel occlusion detection approach. The goal
of this algorithm is to detect vehicles in congested traffic scenes
with occlusion handling. Also we proposed a new proper
feature for vehicle detection to avoid the merging of two or
more objects into one and to improve the accuracy of object
localization. This feature is strong shadow which exists under
any vehicle. Many sequences of real images including noisy
images, cluttered, and images containing remarkable shadows
and occlusions, taken from both scenes of outdoor highways
and city roads, have been taken for evaluating our method.
I
I. INTRODUCTION
NTELLIGENT traffic surveillance systems are
assuming an increasingly important role in highway
monitoring and city road management systems. Their
purpose, amongst other things, is to provide statistical data
on traffic activity such as monitoring vehicle density and
signaling potentially abnormal situations. This paper
addresses the problem of vehicle segmentation in traffic
images including vehicle occlusion.
Some of the related works for analyzing surveillance
images, based on background subtraction methods [1], [2].
Also [3-5] have some methods for occlusion handling in the
video data stream. Occlusion detection can be performed
using an extended Kalman filter that predicts position and
size of object bounding regions. Any discrepancy between
the predicted and measured areas can be used to classify the
type and extent of an occlusion [4], [5]. The early attempts
to solve the occlusion problem involved simple
thresholding, while later methods applied energy
minimization [6] or Markov random field (MRF) models
[7]. In order to find the number of motion classes, K-means
clustering [8] and mixture models [9] were used. More
recently, motion segmentation methods based on active
contours [10] have been proposed. The original activecontour formulation [11] suffers from stability problems and
fixed topology. These issues can be resolved by embedding
the contour into a higher-dimensional function of which it is
a zero-level set [12]. An alternative is to consider all
intensities in a region, as proposed by Mansouri and Konrad
Maryam Sadeghi, B.Sc. Student, Computer Engineering Department,
Iran University of Science and Technology, Narmak, Tehran, Iran, (e-mail:
ma_sadeghi@comp.iust.ac.ir)
Mahmoud Fathy, Associated Professor Computer Engineering
Department, Iran University of Science and Technology, Narmak, Tehran,
Iran, (e-mail: mahfathy@iust.ac.ir).
1-4244-0094-5/06/$20.00 ©2006 IEEE
[13], Jehan-Besson et al [14], and Debreuve et al.[15]. Some
early works using multiple frames includes motion detection
using 3-D MRF models [16] and "video-cube" segmentation
based on marker selection and volume growing [17]. The
other concept of object tracking as spatio-temporal boundary
detection has been proposed by El-Feghali et al. [18] and
[19]. This new multiple-image framework has lead to
interesting space-time image sequence segmentation
methods developed by Mansouri et al.[20], [21] and,
independently, by the authors [22]. In [23] they employed a
straightforward blob tracking algorithm. The advantage of
part-based methods is shown in [24], and the algorithm
known as predictive Trajectory Merge-and-Split (PTMS) in
[25], has been developed to detect partial or complete
occlusions during object motion.
Traditionally, shadow detection techniques have been
employed for removing shadows from background and
foreground but in our work, we used it as a key feature to
vehicles detection and occlusion handling. In our approach
shadow is a key feature and plays very important positive
role. Shadows provide relevant information about the scene
represented in an image or a video sequence. Until now
shadow was a problem for good object detection and
shadows cause the erroneous segmentation of objects in the
scene but now we use special type of shadow (strong
shadow which exists under vehicle) as a key feature for
vehicle detection and occlusion handling. The problem of
shadow detection has been increasingly addressed over the
past years. Shadow detection techniques can be classified
into two groups: model-based and property-based
techniques. Model-based techniques are designed for
specific applications, such as aerial image understanding
[26] and video surveillance [27]. Luminance information is
exploited in early techniques by analyzing edges [28], and
texture information [29]. Luminance, chrominance and
gradient density information is used in [30]. Color
information is used also in [31]. A physics-based approach
to distinguish material changes from shadow boundaries in
chromatic still images is presented in [32]. Authors of [32]
proposed Shadow-aware object-based video processing in
[37]. A classification of color edges by means of
photometric invariant features into shadow-geometry edges,
highlight edges, and material changes is proposed in [33]. In
[34] cast shadow segmented using invariant color features.
In our method, contrary of previous algorithms, shadow is
useful feature to detect vehicles. We use photometric
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characteristic of darkest pixels of strong shadows in traffic
images for occlusion handling.
I. THE PROPOSED APPROACH
During occlusion, visual features of the occluded objects
are not observed and the objects cannot be tracked. We
propose a two step approach to handle the occlusions. The
first step detects the novel feature for detecting vehicle, and
the second step convert picture format to binary with
morphologic techniques and suitable filters.
A. Novel Feature
Our approach is directly based upon the physical
characteristics of shadow pixels. The part of pixels that are
observable under vehicle and between wheels of it is
proposed as a novel feature. This strong shadow is the
darkest part of image and other shadows doesn't influence in
our algorithm because we use an intelligent low-cost
approach to determine useful shadow. Fig.1 shows the
shadow feature existent in any vehicle image taken from a
camera which is used for traffic monitoring.
Photometric and spectral properties of shadows
To describe the spectral appearance of a surface in
shadow, let us consider the physics of color generation. The
appearance of a surface is the result of the interaction among
illumination, surface reflectance properties and the responses
of a chromatic mechanism. This chromatic mechanism is
composed of three color filters in a color camera. To model
the physical interaction between illumination and surface of
objects, let us consider the Dichromatic Reflection Model
[34]-[35]. The radiance [34-36] of the light, Lr(Ȝ ,p),
reflected at a given point p on a surface in the 3D space,
given some illumination and viewing geometry, is
formulated as [34]
Where La(Ȝ), Lb(Ȝ, p), Ls(Ȝ, p) are the ambient reflection
term, the body reflection term, and the surface reflection
term, respectively and Ȝ is the wavelength. The ambient
illumination term is assumed to account for all the light
indirectly reflected among surfaces in the environment and
does not vary with geometry. If there is no direct
illumination because an object is obstructing the direct light,
then the radiance of the reflected light is
(a)
(b)
Fig.1. (a) and (b), two different views of our proposed
shadow feature
In Fig.2 we distinguished this feature in traffic images
B. Theoretical Background
What is a shadow?
A shadow occurs when an object partially or totally
occludes direct light from a source of illumination. Shadows
can be divided into two classes: self and cast shadows. A self
shadow occurs in the portion of an object which is not
illuminated by direct light. A cast shadow is the area
projected by the object in the direction of direct light. In the
following, the relationship between shadows and lit regions
is formalized in order to derive relevant shadow properties.
Which Lr(shadow) represents the intensity of the reflected
light at a point in a shadow region. Let SR(Ȝ), SG(Ȝ) and
SB(Ȝ) be the spectral sensitivities of the red, green, and blue
sensors of a color camera, respectively. The color
components of the reflected intensity reaching the sensors at
a point (x, y) in the 2D image plane are
Where
are the sensor responses, E (Ȝ, x, y) is the
image irradiance [36] at (x, y), and
The interval of summation is determined by Sci(Ȝ), which is
non-zero over a bounded interval of wavelengths Ȝ. Since
image irradiance is proportional to scene radiance [34],[36],
for a pixel position (x, y), representing a point p in direct
light, the sensor measurements are
Fig.2. the shadow feature is observable in real traffic images
Giving a color vector C(x, y)lit=(Rlit, Glit, Blit). Į is the
proportionality factor between radiance and irradiance. For a
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point in shadow the measurements are
In which C(x, y)shadow = (Rshadow, Gshadow, Bshadow). It
follows that each of the three RGB color components, if
positive and not zero, decreases when passing from a lit
region to a shadowed one, that is
Rshadow < Rlit
Gshadow < Glit
Bshadow < Blit
Ambient light can have different spectral characteristics
with respect to direct light [32, 34]. The intensity differences
between shadow and other parts of surface is one of the
photometric characteristics. In traffic scenes, sun is the light
source and vehicles are strong obstacles against sun. We can
model the under-vehicle region which does not receive any
direct sun light with above formulas.
It means that pixels of shadow in under-vehicle region
have just La(Ȝ) and have very low intensity. All other
surfaces in a traffic scene, even surfaces of dark objects such
as black or dark color vehicles, have the body reflection
Lb(Ȝ ,p) and the surface reflection Ls(Ȝ ,p). So this region has
different spectral properties. Also other shadows in traffic
scene have Ls(Ȝ ,p) and more intensity than under vehicle
shadows. We will show in the paper that these shadows will
be eliminated properly.
The size and direction of shadow varies as a result of
movement of the light source (sun) in daytime and traffic
images are affected therein. So, we need a robust approach
to vehicle detection and occlusion handling that effectively
works regardless of light source position. The region under
vehicle has a very strong shadow since vehicles are very
strong obstacles and this region has only ambient light.
Therefore because of the low dependency of this feature to
light source, sun position doesn't have any effect on it and
this region always has the lowest intensity among image
pixels. This property has been shown in Fig.3.
Fig.4. Camera viewpoint for feature detection
For determining our mentioned feature and detecting
vehicles in traffic scenes, viewpoint of camera is very
important and effective. If its angel with horizontal line (ș)
is 90 degree, this algorithm can not work properly. Therefore
we should regulate camera position and viewpoint angle so
that this feature can be observable.
For internal camera calibrating, we can modify the
detection region by regulating Į angle which is a property of
CCD Camera and for external calibrating, height and ș angle
should be regulated. Most of the cameras used for traffic
monitoring or controlling satisfy these conditions.
C. Colormaps
Because of very low intensity of feature region, image
format converting from index to gray with specific colormap
can find out this feature.
A colormap is equivalent to paint-by-numbers and is a
table which contains a small set of color definitions. These
colors are the only ones that are used in the image being
produced and the application maps some value at a pixel to
an allowed color from the colormap.
Matlab has several colormaps that have 64 levels, each
level containing the RGB values that define the color. In
Fig.5 cool colormap of Matlab has been shown.
Fig. 5. Cool colormap of Matlab
Fig.3. Very low dependency of novel feature to light source
position
The key element in detection of this region is the camera
viewpoint. The camera is assumed to be calibrated suitably
to detect the strong shadow of the vehicle. A view of the
camera position is shown in Fig.4.
Colormap is a common scalar visualization technique that
maps scalar data to colors. Colormaps provide a convenient
method for specifying colors through their RGB
components. If intensity of some pixels is very low, we can
find out these pixels with colormap converting .
Many default colormaps are available in Matlab and also
we can customized them. Colormaps can be modified or
created with Matlab Colormap Editor. Scalar values serve as
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indices into a color look up table, i.e., colormap
0
s i < min,
­
°
i = ®
n−1
s i > max,
° ( n − 1 )( s − min) /(max − min)
otherwise .
i
¯
Where n is the length of colormap, typically, n = 256, the
scalar is in [min, max]. Our experiments demonstrated that
the best method to show different spectral properties of
novel feature (strong under-vehicle shadow) is format
conversion with specific colormap. Because, pixels of this
part of image have the lowest intensity of image pixels.
After converting, it can be shown with minimum peak of
colormap. In section B we proved that
Rshadow < Rlit
Gshadow < Glit
Bshadow < Blit
Therefore pixels of strong shadow have minimum
amount of RGB values and they are in down part of Index
image table. When we convert traffic image form Index
format to Grayscale format, minimum values of Index image
will be mapped with minimum peak of colormap. So we can
distinct the region which includes our feature. For showing
this fact, we set some of our experiments result in Fig .6.
No lighting
Flat lighting
Gouraud lighting
Phong lighting
(a)
(b)
Fig.7 (a) Original RGB image, (b) Grayscale image
using JET colormap (Index Æ Grayscale)
After determining this strong shadow, the resulted image
is converted into a binary image using a proper threshold.
Then a noise suppression algorithm is applied on the binary
image and blobs are counted. Due to perspective, the
shadows in the end of image are too small or segmented.
This problem can be resolved by filling the holes, removing
the narrow protrusions and applying morphologic techniques
such as Dilation, Erosion, Closing and Opening. Also
instead of converting RGB image from Index format to
Grayscale format, we can directly convert RGB into
Grayscale, Fig.8.
(a)
(b)
Fig.8 (a) Original RGB image, (b) Grayscale image
using JET colormap (RGB Æ Grayscale)
(a)
(b)
(c)
(d)
As it is obvious in Fig.7 and Fig.8, the under-vehicle
shadow can be used as an effective feature in vehicle
detection. This feature is also useful in occlusion detection.
Our detection is more reliable when using customized
colormaps. Colormaps can be modified or created by Matlab
Colormap Editor. In Fig.9 the final result of algorithm is
illustrated. In this figure we have used a customized
colormap, which is modified from VGA colormap.
Fig.6. influences of format conversion from Index to
Grayscale with different colormaps of Matlab, (a) four
object which is created with Matlab under four different
lighting styles (none, flat, Phong, Gouraud), (b) Index
to Grayscale conversion with Winter colormap, (c)
Index to Grayscale conversion with Pink colormap, (d)
Index to Grayscale conversion with Hot colormap,
(a)
When a RGB image is converted from Index to Grayscale
using a specific colormap, the different spectral property of
under-vehicle region can be precisely observable. We
executed this process on traffic scenes in Fig.7.
(b)
(c)
Fig.9 (a) Original RGB image, (b) Grayscale image
using customized colormap, (c) Final result of algorithm
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D. Experimental Results
To demonstrate the effectiveness of our algorithm, we
have applied it to several real images. The input images are
masked to determine the framework field. There may be
situations where two vehicles in close proximity were first
segmented correctly, but later segmented as one blob, due to
partial occlusion or errors in the background image. Also,
there may be cases where parts of a large vehicle (for
example, a container carrier) are represented by separate
blobs. These and similar situations cause inaccuracies in
measured traffic parameters. Therefore, in order to have
more accurate detection, first we increase image contrast
then morphologic techniques are applied.
Long sequences of real images (noisy, cluttered, and
containing remarkable shadows and occlusions) taken from
both scenes of outdoor highways and city roads have been
considered. The first image in Fig.10 shows a single vehicle
along a road, whereas the second one Fig.11 shows two
vehicles under occlusion that our approach could detect them
and in Fig.12 there is a group of occluded vehicles in
congested traffic which are detected accurately. Finally,
Fig.13 shows a bus in a road that is detected such as other
vehicles.
(a)
(c)
(d)
(d)
(b)
(e)
(c)
(f)
II. CONCLUSION
Fig.10(a) Original image, (b) Masked image,(c)
Grayscale converted from Index image using Cool
colormap, (d) Detected vehicle
(a)
(b)
Fig.13 (a) bus image, (b) Masked image, (c) Increased
contrast,
(d) Grayscale converted from Index image using Cool
colormap,
(e) Grayscale converted from Index image using
customized colormap, (f) Binary detected vehicles
(b)
(c)
(d)
Fig.12 (a) Congested traffic scene, (b) Grayscale
converted from Index image using Cool colormap, (c)
Binary image, (d) Detected vehicles
(a)
(a)
(b)
(c)
Fig.11 (a) Two Occluded Vehicles, (b) Grayscale
converted from Index image using Flag colormap, (c)
Detected vehicles
In this paper we discuss novel occlusion detection
approach. We proposed a new proper feature for detecting
vehicle and improving the accuracy of object localization.
This feature is strong shadow which exists under any
vehicle. Our novel approach to detecting occluded vehicles
has following simple and efficient steps:
1. preprocessing (increasing image contrast)
2. Convert image format from Index to Grayscale
3. Convert image format from Grayscale to Binary
4. Perform suitable morphologic techniques
This approach has several advantages. It is very simple
and low-cost and can help other detection algorithms for
occlusion handling. Accuracy of this approach in real images
including noisy images, cluttered, and images containing
remarkable shadows and occlusions, is very good.
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III. FUTURE WORKS
Future work involves making the algorithms more robust
for occlusion and improving its performance. The current
results are encouraging and further work is being done to
make the system more practical. Work can be done for
enhancing the algorithms more suited to the non-static
cameras in intelligent vehicles. This system if coupled with
other algorithms for detecting and classification, can help in
solving occlusion problem and thus help in accurate traffic
monitoring and controlling.
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