Journal of Signal and Information Processing, 2013, 4, 299-307
http://dx.doi.org/10.4236/jsip.2013.43038 Published Online August 2013 (http://www.scirp.org/journal/jsip) 299
Depth-Aided Tracking Multiple Objects under Occlusion
Anh Tu Tran, Koichi Harada
Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan.
Received May 12th, 2013; revised June 15th, 2013; accepted July 12th, 2013
Copyright © 2013 Anh Tu Tran, Koichi Harada. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
In this paper, we have presented a novel tracking metho d aiming at detecting objects and maintaining their la-bel/iden-
tification over the time. The key factors of this method are to use depth information and different strategies to track ob-
jects under various occlusion scenarios. The foreground objects are detected and refin ed by backgroun d sub traction and
shadow cancellation. The occlusion detection is based on information of foreground blobs in successive frames. The
occlusion regions are projected to the projection plane XZ to analysis occlusion situation. According to the occlusion
analysis results, different objects’ corresponding strategies are introduced to track objects under various occlusion sce-
narios including tracking occluded objects in similar depth layer and in different depth layers. The experimental results
show that our proposed method can track the moving objects under the most typical and challenging occlusion scenar-
ios.
Keywords: Visual Tracking; Multiple Object Tracking; Stereo Tracking; Occlusion Analysis
1. Introduction
Object tracking in the video sequence has played an im-
portant role in a research area of computer vision and a
wide range of applications, such as video monitoring and
surveillance, video conferencing and video summariza-
tion. Based on different camera configurations, objects
can be tracked by using a single camera or stereo/multi-
ple cameras. Object tracking with a single camera has
studied in many literatures and different methods have
been developed such as tracking by model-based tracking
method [1], appearance-based methods [2-4], feature-
based tracking [5], and statistical methods [6-8]. Many
algorithms can obtain good results in so me cases, such as
when the targets are separated. However, multiple object
tracking is still a challenging task due to the non-rigid
motion of deformable object, persistent occlusion and the
dynamic change of object attributes, such as color distri-
bution, shape and visibility. In the real scene, occlusion
between objects often occurs. For example, in typical
surveillance scenario a person is partially or fully oc-
cluded by other people. Unfortunately, these occlusions
lead to failed tracking. Some classical frameworks have
been extended to track multi objects. In the multi-object
tracking system [9], the level set method is used to han-
dle contour splitting and merging. Extensive methods, i.e.
Monte Carlo based probabilistic methods [10], game
theory based approaches [11] and appearance model
based deterministic methods [12,13] have been presented
to solve the mutual occlu sion p roblem. Ano ther attractive
research direction is stereo or multiple camera based
method. While object detection and tracking with a sin-
gle camera are well-explored topics, the use of multi-ca-
meras technology for this purpose has attracted much
attentions recently du e to the availability and low price of
new hardware. A multi-camera system observes the scene
from two or more different views, and obtains more
comprehensive information than a monocular camera
system, which can take the advantage of depth informa-
tion to improve the tracking system performance. Some
tracking methods focus on usage of depth information
only [14], or usage of depth information on better fore-
ground segmentation [15], or usage depth information as
a feature to be fused in a maximum likelihood model to
predict 3D object positions [16].
In this work, we have presented a novel tracking
method aiming at detecting objects and maintaining their
label/identification across video frame sequence. The
main points of this method are to use depth information
and different strategies to track objects under various
occlusion scenarios. Figure 1 shows the flowchart of our
tracking system.
The rest of this paper is organized as follows: Section
2 presents the proposed tracking method. Section 3

Depth-Aided Tracking Multiple Objects under Occlusion 300
shows experimental results; and, finally, Section 4 con-
cludes this paper.
2. Proposed Synthesis Method
Our proposed tracking system is shown in Figure 1 and
it consists of below main steps.
2.1. Depth Estimation
Depth estimation aims at calculating the structure and
depth of objects in a scene from two views or a set of
multiple views. This topic has been attracted extensive
attentions in research communities. A comprehensive
survey and evaluation of dense two-view stereo matching
algorithms can be found in [17].
In this work, depth is estimated based on block match-
ing algorithms proposal in [18]. This block matching
technique is a one-pass stereo matching algorithm that
uses a sliding sums of absolute differences window be-
tween pixels in the left image and the pixels in the right
image. An example of depth image is shown in Figure 2.
2.2. Foreground Segmentation and Shadow
Cancellation
Our method performs foreground segmentation to speed
up the process of object tracking. There are many fore-
Figure 1. The flowchart of proposed tracking method.
Figure 2. Color image and depth image.
ground segmentation algor ithms for instance of Gaussian
mixture model [19,20]. In our method, we use simple
technique based on absolute differences between current
image and background image.
In some cases, we have the fixed cameras observing
the scene, so we may have an image of the background
of the scene. However, in most case this background is
not readily available. Moreover, the background scene
often evolves over time because for example the light
condition might change or because of new object could
be added or removed from the background. Therefore, it
is necessary to dynamically build the background model
by regularly updating it. This can be accomplished by
computing moving average using the following formula:



1
1
tt
tp


 , (1)
where, is pixel value ate a given time ,
t
pt

1t


is
the current average value, and

is called the learning
rate and it defines the influence of the current value.
In our method, first a color background model is cre-
ated by computing a moving average for each channel (R,
B and G channels of color image) of each pixel of in-
coming frames (around 10 frames). The decision to de-
fine a foreground pixel is simply based on comparing the
current frame with background model and then updating
this. Specifically,
  

c
c
0if I,
Iotherwise
bg H
pIp t
Fp p





(2)
where,


F
p is value of pixel in foreground image, p




cbg
I
pIp repres ents the abso lute color differ ence
between the color value at pixel of current frame p


c
I
p and the color value of pixel of background
frame
p


bg
I
p of R, G, B channels.
H
t is threshold and
for the each color channel this threshold can be set to


0.3*bg
I
p. An example of foreground image is shown
in Figure 3.
However, the segmented foreground image includes
noise affected by the shadow. The shadow regions are the
parts of moving objects. Shadow detection and removing
will be used to refine the foreground . To avoid the effects

Depth-Aided Tracking Multiple Objects under Occlusion 301

blob area


BA , and the average depth value of blob


min
Z. Blobs are also given temporally identification


I
D.
of shadows, a shadow detection described in [21] is em-
ployed. More detail about this method, please refer to
[21]. The result of the shadow detection is shown in
Figure 4.
We define two kinds of distance: the distance between
blob and blob in the same frame and the dis-
tance between blob of frame and blob of fra-
ijt
i tj
2.3. Blobs Extraction and Blobs’ Information
Store
me


1t

.
First, we try to extract the blobs of objects from seg-
mented foreground image. Blob extraction is performed
on the foreground binary image by connected component
labeling using CvBlobLib library [22]. The foreground
binary image can obtained from simple threshold opera-
tion followed by the application of eroded and dilated
operation on the segmented foreground image. CvBlob-
sLib provides two basic function alities: extracting 8-con-
nected components, referred to as blobs, in binary or
grayscale images using Chang’s contour tracing algo-
rithm [23], and filtering the obtained blobs to get the ob-
jects in the image that we are interested in. In our method,
we remove any blobs that have area small than 100 pix-
els.
The distance


,
tt
di j between blob i and blob
j
in the same frame is computed by:
t


2
,,
tt tt
tti jij
di jxxyy
2
(3)
where


,
tt
ii
x
y and


,
tt
j
j
x
y are the center coordina-
tion of blob and blob at frame t, respectively. ij
Similarly, a distance



1
,
tt
Di j

t
between blob i of
frame and blob of frame is calculated by: tj

1






11
22
1
,,
tt tt
tijij t
Di jxxyy

  (4)
For every frame, after extracting blobs, information
about blob is stored in a structured record for later proc-
essing steps. The blob’s information includes total num- where


,
tt
ii
x
y and
 


11
,
tt
jj
xy

are the center coor-
dination of blob at frame and blob at frame i tj
ber of blob

, blob’s center coordination NB


,
x
y,


1t

correspondingly.
2.4. Occlusion Detection
We detect the occlusion in current frame according to
blobs’ information at frame t and previous frame
t


1t

. It is based on two clues. The first clue comes
from the shortest distance between blobs at the same fra-
me


1t

and the second one is the difference of num-
ber of blobs at frame and . First, we find the
shortest distance
t


1t

(a) (b)




1t1
di j

,
t between blobs in frame


1t

, assuming that it occurs between blob m and
Figure 3. Foreground segmentation. (a) input image; (b)
segmented foreground image.
blob , i.e. n
 



 
min 11 11
,min,
tt tt
dnmdi j
 
.
We define an occlusion flag . This flag gets
occ_s t art
f
value t if occlusion is found at frame and otherwise it
gets value t 1

. Specially (see Equation (5)),
(a) (b) where,

1t
NB

and are the total number of blobs
in frame
t
NB


1t

and frame respectively. is tthreshold d
Figure 4. Shadow regions detection. (a) segmented fore-
ground image; (b) shadow detection (the blue color pixels
are the detected shadow). the threshold of blob distance at the same frame.
 


minthreshold( 1) 11
occ_start
ifand ,
1otherwise
tt- tt
t dn,md NBNB
f





(5)

Depth-Aided Tracking Multiple Objects under Occlusion 302
Similarly, we also detect when the occlusion termi-
nates. The end of occlusion is checked based on the
shortest distance between blobs at the current frame
and the difference of number of blobs at current frame
t
and previous frame . We define the end of occlu-
sion flag as following:

1t




1
t

occ_end
f


minthreshold 1
occ_end
if and,
1otherwise
tt t
t
tdn,md NBNB
f





(6)
2.5. Object Tracking
According to the result of occlusion detection, the track-
ing objects can be dividing into two types: tracking ob-
jects without occlusion and tracking objects under occlu-
sion.
2.5.1. Tracking Objects without Occlusion
The video obj ects corr esponden ce und er non-o cclusio n is
obtained through the shorted distance be-


1
,
tt
Di j
tween blobs in previous frame and blobs in the current
frame. This distance between blobs in previous frame
and blobs in the current frame is calculated by Equation
(4). For instance, once a foreground blob at frame
m

m
t
tB

1t
finds its corresponding blob in frame
, its label or identification is updated corre-
spondingly to the of blob . Specially,

1
n
t
B
)(ID

1
n
t
ID B









1
11
thres 1
of of
,min ,1,2,,
if,
mn
tt
mn mj
tt
tt
mn
tt
ID B ID B
DB BDB,BjNB
DB BD










(7)
where k
j
B denotes the blob at frame ; kj
 1t
NB

is
number of blobs in frame


1t; is distance
thr
Des
threshold.
2.5.2. Tracking Objects under Occlusion
The main idea of our tracking method is that the object
label or identification (ID) is maintained constantly dur-
ing occlusion and after they switch their positions.
When occlusion occurs, we can detect and extract the
occlusion region. We also can detect and separately ex-
tract a list of objects that are non-occlusion objects in
previous frame but overlaying each other in current
frame.
In order to track the objects under occlusion, depth in-
formation is used to analysis the occlusion situation. First,
the occluded regions are projected to the ground plane
X
Z according to their horizontal position and their
depth gray level (more detail in the next subsection).
Then according to the
X
Z plane, the occlusion objects
can be divided into two types based on the depth ranges:
1) in the different depth layer or 2) in the same depth
layer. We are dealing with these situations as following
parts.
1) Project Occlusion Regions into Ground Plane
XZ
Each foreground pixel in occlusion region has in-
formation obtained from depth map. These pixels are
projected to the ground plane
D3
X
Z according to their
horizontal position and their depth gray level, where X is
the width of the depth map and the range of Z is [0,255].
The projected point, which is located at

,

x
z, is de-
fined as


,pxz. The value at position of pro-
jection plane is the total number of points at position x in
the depth maps that have same gray level (depth value z).
Figure 5 illustrates the image plane XY and ground plane
XZ.

,pxz

In order to remove noisy points, if the value at point


,pxz is less than threshold 1
T the point


,pxz
will be discarded. Then we also apply morphological
operations (dilating and eroding operation) to remove
noisy points and connect nearby points. The remaining
points in XZ plane are grouped in to the blobs that are
based on connected component analysis technique using
CvBlobLib library [22]. If a projected blob is small than
threshold 2, it is consider a noise and it will be re-
moved. T he pr ojecte d blo bs are define d as
T



1, 2,,,
j
PB jm where m is total number of pro-
jected blobs. Each projected blobs
j
PB is mask as ob-
ject regions. Figure 6 shows an example of projected
blobs in XZ plane.
As mention before, according to the projected blobs in
XZ plane, the occlusion objects can be divided into two
types based on the depth ranges: 1) in the different depth
layers or 2) in the one depth layer.
2) Tracking Occluded Objects in Different Depth Lay-
ers
Figure 6 shows the case of occluded objects is in dif-
ferent depth layers. Once occluded objects have different
YZ
X
Figure 5. The image plane XY and ground plane XZ .

Depth-Aided Tracking Multiple Objects under Occlusion 303
(a) (c)
(b) (d)
Figure 6. Projected for eground blobs in XZ plane. (a) input
image; (b) foreground blob (occlusion region); (c) occlusion
region (depth image); (d) projected blobs in XZ plane.
depth layer, they can be segmented in color image by
means of their depth ranges. An example of color image
segmentation by means of depth ranges is shown Figure
7. In our method, object correspondence under different
layer is based on Bhattacharyya distance [24] between
the color histograms. In statistics, the Bhattacharyya dis-
tance measures the similarity of two probability distri-
butions. In our case, Bhattacharyya distance represents
the similarity between two normalized histograms. The
Bhattacharyya distance is calculated by:
1
1
N
bb
b
BDp q


,

k

(8)
where, BD denotes Bhattacharyya distance; p and q are
the two normalized color histograms; N is number of bin
in histogram.
Let is the occluded objects at
frame and denotes the ex-

12
,,,
kkkm
OOO O
k

12
nn
UU,,,
n n
m
U U
isting non-occluded objects in previous frame n (the
frame before occlusion is found). The color histogram of
each occluded objects and existing non-occluded objects
are k
i
O
H
and n
j
U
H
, respectively. In this paper, color
histograms are created from hue component of
color space. Figure 8 shows an example of the color his-
togram.
HSV
For each occluded object , we calculate the Bhat-
tacharyya distance between color histo-
k
i
O

,
kn
i
U

j
BD O
gram of this object and color histogram of every object
n
j
U in and then find the shortest distance .
n
Umin BD
(a) (b)
(e) (c)
(f) (d)
Figure 7. An example of image segmentation by means of
depth ranges. (a) input image; (b) foreground blob (occlu-
sion region); (c) occlusion region (depth image); (d) Pro-
jected blobs in XZ plane (occluded objects in different depth
layers); (e) segmented object based on depth layer 1 (red
color in d); (f) segmented object based on depth layer 2
(blue color in d).
Hue
Frequency
(a) (b)
Figure 8. An example of color histogram. (a) color image; (b)
color histogram.
The Bhattacharyya distance is computed

,
kn
ij
BD OU

according to Equation (8), specially:

 
1
,1 kn
ij
N
kn
ij OU
b
BD OUHbHb


, (9)
The occluded object will update its accord-
ing to
k
i
OID
n
j
U if


kn
ij
Umin ,BD OBD. Figure 9 shows the
numeric results of calculating Bhattacharyya distance

Depth-Aided Tracking Multiple Objects under Occlusion 304
(a) (b) (c)
1
O
H
2
O
H
(d) (b1) (c1)
Dista nce : 0. 32 88
21
OU
BD(H ,H)
1
U
H
Distance: 0.4106
11
OU
BD(H ,H)
1
U
H
(e) (e1)
12
OU
BD(H ,H)
2
U
H
Distance: 0.4253 Distance: 0.3367
22
OU
BD(H ,H)
2
U
H
(f) (f1)
Figure 9. Bhattacharyya distance between two histograms.
(a) input frame (having occlusion objects); (b) segmented obj.
1 in input frame (O1); (c) segmented obj. 2 in input frame
(O2); (d) Previous frame (before occlusion happened); (b1)
Histogram of object O1 in (b) (); (c1) Histogram of ob-
ject O2 in (c) (); (e) object 1 before occlusion happened
(U1); (e1) Histogram of object U1 in (e) (); distance
between and is 0.3288; distance between
and is 0.4106; (f) object 2 before occlusion happened
(U2); (f1) Histogram of object U2 in (f) (); distance
between and is 0.4253; distance between
and is 0.3367.
1
O
H
2
O
H
1
U
H
2
U
H
1
O
H
1
1
O
H
2
1
U
H
2
U
H
2
O
H
2
O
H
U
H
U
H
between two histograms.
Figure 10 illustrates an example of tracking occluded
objects in diffe rent de pth laye rs.
3) Tr acking Occlude d Ob j ects in One Depth Laye r
Figure 11 shows an example of occluded objects in si-
milar depth layer.
When occlusion objects have similar depth range or
(a) (b)
Figure 10. Tracking occluded objects in different depth la-
yers. (a) Input frame (having occlusion objec ts); (b) Output
(tracking occlusion objects in different layers).
(a) (b) (c)
Figure 11. An example of tracking occluded objects in one
depth layer. (a) input frame (having occlusion objects); (b)
occlusion region (depth image); (c) projected blobs in XZ
plane (occlusion objects in one depth layer).
full occlusion, it is difficult to segment and track multiple
objects as above technique. To deal with this problem,
we propose the tracking method based on camshift (Con-
tinuously Adaptive Mean shift) algorithm [25] as fol-
lowing part.
Assuming that there are occluded object m


1, 2,,
k
i
Oi m in the occlusion region , their
k
occ
R
existing corresponding tracks in the previous frame are

1k
j
U

. Our algorithm has following steps:
1) Pre-computing the color histogram U
H
for every
existing track n
j
U, and the color histogram
R
H
for
occlusion region. Here we calculate a hue histogram
from
H
SV color space.
2) Based on the average depth values, sorting the
list of object


1k
j
U

so that the object with biggest depth


1
foremost
k
U (i.e. the shortest distance from camera) goes
first.
3) Calculating a back projection of a hue plane of oc-
clusion region using the pre-computing histogram of

1
foremost
k
U
k
occ
R
ID
. Based on the back projection image, finding in
the corresponding object of using cam-
shift algorithm. Label it as with according
to the of .

1
foremost
k
U
ID
foremost
k
O

1
foremost
k
U
Figure 12 shows an example of projection image.
4) Removing the in . Selecting the next
foremo st
k
Ok
occ
R
track in the sorted

 1k
j
U

list and running step (3) to

Depth-Aided Tracking Multiple Objects under Occlusion 305
(a) (d)
(b1) (b2) (e)
(c) (f)
Figure 12. An example of projection image. (a) Previous
frame (before occlusion happened); (b1) Object 1 before
occlusion happened (with bigger depth); (b2) Object 2 be-
fore occlusion happened; (c) Histogram of object 1 in (b1);
(d) input frame (having occlusion objects); (e) occluded
objects in input frame; (f) Projection image of occluded
region in input frame using pre-computing histogram in (c).
find the next k
j
O in .
k
occ
R
5) Repeating step (4) until all object in in
k
Ok
occ
R
finds their corresponding track.
Figure 13 demonstrates a result of tracking occluded
objects in one depth layer.
In practice, when projecting partly occluded objects or
fully occluded objects with similar depth ranges into
X
Z plane, we will obtain only one blob/region in
X
Z
plane. In the case, the objects in similar depth range are
partly occluded, the above algorithm will work well (see
Figure 13). However, the fully occluded objects current
frame t will reappear as partial occluded objects bind
their occluder in the later frame, so it will be tracked by
our method (see example in Figure 14).
3. Experimental Results
In this section, we show the experimental results to
evaluate the proposed tracking method. We evaluate the
tracking performance by the capability of detecting and
maintaining constant of foreground objects during
the occlusion and after the occlusion over.
ID
The proposed tracking method has been test on some
video sequences. The input of out method is a pair of
video sequence and the output is the left video sequence
in which has a set of moving objects labeling with
and bounding boxe s with different color.
ID
Figure 15 shows results of tracking non-occlusion ob-
jects. In the example 15(a), in the 3 frames (left to right)
each object appears one after another and in the fourth
frame, object with has gone out the scene. These
results illustrate the ability of our algorithm in detecting
object, assigning and maintaining the objects .
ID
ID
We demonstrate the result of tracking of occluded ob-
jects in different depth layers in Figure 16. Our proposed
method can successful detect the object under partial
occlusion. All of objects have constantly label over the
(a) (b)
Figure 13. An example of tracking occluded objects in one
depth layer. (a) Input frame (having occlusion objects); (b)
Output (tracking occlusion objects in differe nt layers).
Figure 14. Tracking fully occluded objects. “Room sequen-
ce”: from left to right, the frame indexes are 67, 72, 73 and
74, respectively.
(a)
(b)
Figure 15. Result of tracking non-occluded objects. (a)
Tracking of non-occluded obje cts in “Gym sequence” (from
left to right, the frame number are: 34, 42, 60 and 201); (b)
Tracking of non-occluded objects in “Room sequence”
(from left to right, the frame number are: 0, 19, 113 and 250,
respectively.

Depth-Aided Tracking Multiple Objects under Occlusion 306
time even they are moving in variety of pose and posi-
tion.
Tracking the occluded object under the similar depth
layer is shown in Figure 17. These examples show that
the proposed algorithm can detect and track a partial oc-
cluded object that is equivalent to at least one half a hu-
man body.
Figure 14 shows the case an object is fully occluded
and in the similar depth layer with its occluder and our
system cannot detect it. However, in the future time when
this object reappears as partial occluded object, the sys-
tem can detect and maintain its ID. This example demon-
strate the capability of proposed algorithm in term of
maintain constant label for object over the video se-
quence.
4. Conclusions
In this paper, we have presented a novel tracking method
aiming at detecting objects and maintaining their ID over
the time. The key factor of this method is to use depth
information to track objects under various occlusion
scenarios. Different object tracking strategies are applied
(a)
(b)
Figure 16. Tracking occluded objects in different depth
layers. (a) “Gym sequence”: from left to right, the frames
indexes are 65, 73, 87 and 145, respectively; (b) “Room se-
quence”: from left to right, the frame indexes are 127, 129,
132 and 135, respectively.
(a)
(b)
Figure 17. Tracking occ luded objects in one depth lay er. (a)
“Gym sequence”: from left to right, the frames indexes are
206, 207, 208 and 209, respectively; (b) “Room sequence”:
from left to right, the frame indexes are 236, 237, 238 and
239, respectively.
according to occlusion situation including finding corre-
sponding objects based on Bhattacharyya distance be-
tween two histograms and using a camshift based algo-
rithm with the help of object depth ordering. The ex-
perimental results have confirmed the capability of our
proposed objects tracking algorithm under the most typi-
cal and challenging occlusion scenarios.
However, the proposed algorithm can work only in an
indoor or medium sized environment since the reliability
of depth information diminishes in proportion to the dis-
tance from the camera and only when the moving veloc-
ity of objects is slow. In the future work, to construct a
robust moving object tracking system in both indoor and
outdoor environment, we will study to use more object’s
features to classify and track the objects.
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