International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 2 – Sep 2014
An Interactive Video Surveillance System for
Detection and Classification of Moving Vehicles
S. B. Kulkarni#1, Sharada K. S#2, U. P. Kulkarni#3, Nagaraj. M. Benakanahalli#4
#1
#2
Professor, Department of Computer Science & Engineering, SDMCET, Dharwad, Karnataka, India
Lecturer, Department of Computer Science & Engineering, SKSVMACET, Laxmeshwar, Karnataka, India
#3
Professor, Department of Computer Science & Engineering, SDMCET, Dharwad, Karnataka, India
#4
Asst., Professor, Department of Automation and Robotics, BVBCET, Hubli, Karnataka, India
Abstract — Traffic controlling and monitoring are the major
issues in today’s world as the number of vehicles is increased
tremendously. So it is imperative to develop a system so as to
have an efficient traffic controlling mechanism by knowing the
information about the number of vehicles moving on the
highways. And also it is better to know the categories of the types
of vehicles moving on highways as the important information for
the concerned traffic authority to monitor the traffic. The main
objective of the work is to develop an interactive system that can
count and classify the vehicles for better traffic management.
The proposed algorithm which is able to detect the moving
vehicles on the road and classify them into light and heavy
vehicles and also it interacts with the user to tag the detected
vehicles and produces the count of vehicles passing on the
highway. The system accepts an integer value 0 for light vehicles,
1 for heavy types of vehicles for newly detected vehicles. In this
paper the preprocessed video is used as input, for which the
improved adaptive Gaussian
mixture model is used for
background subtraction and detection of vehicles. Then the
design of interactive system proceeds with extraction of
geometrical features for the detected vehicle. Based on the
geometrical features extracted the ratio is calculated. The
reference ratio is set for the corresponding vehicle type detected.
When the vehicles of different type are detected, the reference
ratio values are compared for the detected vehicle type and the
vehicles are classified. The respective vehicle type is also counted.
Keywords— Background subtraction, improved adaptive
Gaussian mixture model, geometrical features, reference ratio
I. INTRODUCTION
Vehicle detection and classification is a popular research
technique nowadays. The increasing population and the
transport system resulted in a huge usage of vehicles of
different types. The increased number of vehicles moving on
roads promoted the traffic over the highways. To control
traffic, the signals video surveillance is used which need
manual intervention to fetch the information about the number
or the count of vehicles that are moving at a particular
location. And when the problem of video surveillance and
monitoring is considered, the position of the camera should
not affect the working of the system. The efficient system
should be designed in such a way that it should not be affected
by visual field or whatever lighting effects occur. It is
desirable that the system adapts to gradual changes of the
appearance of the environment as the light intensity typically
varies in day. It should also be capable of dealing with
ISSN: 2231-5381
movement through cluttered areas, objects overlapping in the
visual field, shadows, lighting changes, and effects of moving
elements of the scene, slow-moving objects, and objects being
introduced or removed from the scene. Traditional approaches
based on back grounding methods typically fail in these
general situations.
To achieve the tasks of a surveillance system, target
detection is a special form of dimensionality reduction. As
well finding moving objects in image sequences is also an
important task in computer vision. The numerous approaches
to this problem vary in the type of background model used
and the procedure used to update the model. One such method
to detect the target in video surveillance is to use the method
of background subtraction. Background Subtraction is one of
the most widely used techniques to detect moving objects
from static scene [1]. Since background subtraction is a lowlevel task, it should consider two aspects: Accuracy and
Computational resources (time and memory). First, accuracy
is critical because the output of the background subtraction is
used for other high tasks, such as tracking, recognition and
also classification. Second, computational resources used for
background subtraction are critical since the resources
remaining after this low-level task should be used for high
level tasks, and is preferable as a means of implementing this
task in real-time embedded systems such as smart cameras.
Therefore, it is important for the background subtraction
method to obtain high accuracy and low resource
requirements at the same time.
To develop a proposed system to be robust two of the major
steps are considered. The first one is background modeling
which results in foreground detection. Improved adaptive
Gaussians mixture model is used to develop the background
modeling. The second step is classification of the detected
vehicles .This involves geometrical features extraction. The
resulting classification system aimed towards a simple design
to perform detection of the vehicles on roads and classify
them into small and heavy vehicles with the count.
II. RELATED WORK
Detection, counting and classification of vehicles in a video
have become a potential area of research due to its numerous
applications to video-based intelligent transportation systems
[2]. Vehicle type recognition is a relatively new research
domain, which begins to interest various laboratories in the
world. Vehicle classification is a very important, as accurate
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 2 – Sep 2014
identification of the moving target is the basis for subsequent problem of eliminating apparent changes in image reflectance,
traffic management analysis. At present, many approaches the improved adaptive Gaussian mixture model[10] is applied
have been proposed to automatically classify vehicles in to achieve the foreground detection. Once the foreground is
images and videos. A.J.Liptonet.al. [3] proposed an approach detected the ratios are calculated and corresponding reference
purely based on a target's shape. R.T.Collinset.al.[4]used ratio is set for different type of vehicles. The tagging of
viewpoint-specific neural networks to classify the moving vehicle type for each of the detected vehicle is done, based on
object to be people and vehicles which using spatial features tagging the moving objects into different types of vehicles
of image blob dispersing (perimeter*2/area), image blob area, such as light and heavy vehicles. And the proposed system
apparent aspect ratio of the blob bounding box and camera provides an interactive environment to perform dynamic
zoom. This method is based on training, which has used a lot detection based on tags given by the user i.e., 0 for light
of features and has high complexity. The work of A. Salinger vehicles and 1 for heavy vehicles.
and L. Wilson[5] and Javed and Shah[6] presented a method
Input Video
for classifying moving object as rigid or non-rigid based on
the similarity of their appearance over multiple frames. When
Pre-processing
morphological operations are applied to the blobs for
improving segmentation this method may not be reliable. R.
Cutler and L. Davis [7] described new techniques using image
Foreground vehicle detection using
similarity to detect and analyze periodic motion, which may
IAGMM Background Modeling
don’t work when the motion is non-periodic.
Y.Bogomolovet.al.[8] used hybrid features combining motion
and appearance features. L.B.Chen.et.al. [9] used features of
Geometrical Feature Extraction
from detected Vehicle
objects size, velocity, location and differences of Histograms
of Oriented Gradients to classify people from vehicles. This
Determine Ratio (DR)
approach use complicated features to classification based on
tracking results, and needs to calibrate the size feature, which
is hard to transplant to other scenes.In order to classify the
Receive Tags from the user (0-Light
vehicle, 1-Heavy vehicle) and Set
objects , first background modeling is needed. Background
respective Reference Ratio (RR) for light
modeling is generally done by analyzing some of the regular
and heavy vehicles
statistical characteristics and then object is detected by
comparing the current frame with the modeled background.
However, though it sounds simple, but this technique seems
DR=RR
to be inadequate when dealing with complex environment.
No
Most of
the available non-adaptive methods of
Yes
background subtraction cannot be used in surveillance
Classification of vehicles as
because it requires manual initialization. And in surveillance,
Light and Heavy vehicles
it is not possible to have a pre-defined set of background
model before-hand. So, it is very important that the
Fig. 1 Flow chart for Vehicle Classification
background model is adaptive and robust for the detection of
the objects in the foreground.
A. Pre-processing
The proposed methodology uses video images of vehicle
III. PROPOSED METHODOLOGY
moving on road segments which are captured from the camera
This research work presents an efficient algorithm that that is placed on the elevation on road side. The initial task of
performs two tasks namely vehicle detection and vehicle vehicle classification is to convert true colour input image into
classification. It detects the presence of vehicle in the video gray level image. Input frame I = [IR IG IB] T, is combination
and classifies it into one of the known class. The proposed of the three colour components Red, Green and Blue. The
methodology involves several steps as shown in Figure 1.The input colour frame is converted into gray level image IG,
work starts with the pre-processing where the true colour according to luminance converter [11] so as to get the
input image is converted into gray level image. Then it enhanced images.
proceeds with background subtraction. There are many
approaches developed for background subtraction as discussed B. Background Modeling using Improved Adaptive Gaussian
in the survey work. For the proposed work improved adaptive
Mixture Model
Gaussian mixture model is chosen so as to overcome the
Finding moving objects in video sequences is one of the
problems with lighting conditions such as the illumination most important tasks in surveillance. For many years, the
results from the lighting conditions present when the image is obvious approach has been first to compute the stationary
captured, and can change when lighting conditions change and background image, and then to identify the target as these
reflectance results from the way the objects in the image pixels in the image vary significantly from the background.
reflect light, and is determined by the intrinsic properties of
The numerous approaches to this problem vary in the
the object itself, which does not change. For our given
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 2 – Sep 2014
type of background model used and the procedure used to describe the Gaussian components. The covariance matrices
update the model. There are several popular approaches for are assumed to be diagonal and the identity matrix I has
background subtraction such as GMM [15]. But the results proper dimensions. The mixing weights denoted by
are
obtained for this algorithm had very high false positives and non-negative and add up to one. Given a new data sample
some false negatives. The foreground object was getting at time t the recursive update equations are given by (5), (6)
registered but large number of false positives made it very and (7).
difficult to extract the foreground object. Morphological
(5)
operations such as erosion and dilation also failed to help
(6)
them in reducing the false positives. Even when there is no
foreground object in the scene, the false positives that they got
(7)
in resultant background subtraction frame after GMM based Where
instead of the time interval T that
classification process were very high. So to overcome this was mentioned above, here constant α describes an
problem the proposed methodology uses improved Gaussian exponentially decaying envelope that is used to limit the
mixture model for the background subtraction.
influence of the old data. The same notation having the mind
In order to choose the background model it is important to that approximately α =1/T. For a new sample the ownership
know which approach should be chosen so as to achieve
is set to 1 for the ‘close’ component with largest
and
foreground detection with a good accuracy. The proposed
the others are set to zero. The squared distance from the m-th
method uses an approach known as improved adaptive component is calculated as shown in equation (8).
Gaussian mixture model given by Z. Zivkovic [1] for
(8)
background subtraction which is the extension of the basic
GMM approach. To have the overview of the Gaussian If there are no 'close' components a new component is
Mixture Model (GMM) which is a parametric probability generated with equations (9) and (10).
density function represented as a weighted sum of Gaussian
(9)
component densities [13]. GMMs are commonly used as a
(10)
parametric model of the probability distribution of continuous
Where
is
some
appropriate
initial
variance.
If the
measurements. GMM parameters are estimated from training
maximum
number
of
components
is
reached
we
discard
the
data using the iterative Expectation Maximization (EM)
component
with
smallest
.
algorithm or Maximum estimation from a well-trained prior
model. The related work discussed above says that GMM got The foreground object will be represented by some additional
. Therefore approximate the
failed to adapt the illumination changes and also to identify clusters with small weights
background
model
by
the
first
B
largest clusters given by
the foreground object, to resolve these problems improved
equation
(11).
Gaussian Mixture Model is chosen and it is discussed below.
The enhanced images are fed as input to construct the
(11)
background model. This paper uses impproved adaptive If the components are stored to have descending weights
Gaussian mixture model approach [1] so as to build a then,
background model to track the objects. It helps to create
foreground frames to perform upcoming operations such as
Where cf is a measure of the maximum portion of the data
feature extraction and classification. In the approach, the
that can belong to foreground objects without influencing the
illumination in the scene could change suddenly. A new object
background model. For example, if a new object comes into a
could be brought into the scene or a present object removed
scene and remains static for some time it will probably
from it. In order to adapt to changes update the training set by
generate an additional stabile cluster. Since the old
adding new samples and discarding the old ones. Choose a
background is occluded the weight
of the new cluster
reasonable time period T and at time t, given by equation will be constantly increasing. If the object remains static long
(1).
(1)
For each new sample update the training data set and
re-estimate given by equation (2).
(2)
Among the samples some values that belong to the
foreground objects and should denote this estimate as equation
(3).
(3)
GMM with M components given by equation (4)
(4)
Where
are the estimates of the means and
are the estimates of the variances that
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enough, its weight becomes larger than cf and it can be
considered to be part of the background. By looking at
equation (4). We can conclude that the object should be static
for approximately
frames.
The weight
describes how much of the data belongs to
the m-th component of the GMM. It can be regarded as the
probability that a sample comes from the m-th component and
in this way the
define an underlying multinomial
distribution. Let us assume that we have t data samples and
each of them belongs to one of the components of the GMM.
Let us also assume that the number of samples that belong to
the m-th component is given by equation (13).
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(13)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 2 – Sep 2014
Where
are defined in the previous section. The
assumed multinomial distribution for
gives likelihood
function
. The mixing weights are constrained (21)
to sum up to one. Take this into account by introducing the Since we expect usually only a few components M and cT is
. As mentioned we set 1/t to
Lagrange multiplier λ. The maximum likelihood estimate small we assume
α
and
get
the
final
modified
adaptive
update equation (22).
follows from equation (14).
(22)
(14)
This equation is used instead of this equation
After getting rid of
we get equation(15)
.
(15)
After each update needs to be normalize
so that it
The estimate from t samples we denoted as
and it can is up to one. Then start with GMM with one component
be rewritten in recursive form as a function of the estimate centered on the first sample and new components are added as
for t - 1 samples and the ownership
of the last mentioned in the previous. The Dirichlet prior with negative
weights will suppress the components that are not supported
sample given by equation (16).
by the data and discard the component m when its weight
(16)
becomes negative. This also ensures that the mixing weights
If the influence of the new samples is fix by fixing 1/t to α
we could require
= 1/T to get the update equation (4). This fixed influence of stay non-negative. For a chosen
that
at
least
c
=
0.01*T
samples
support
a
component
and got
the new samples means that rely more on the new samples and
the
result
.
the contribution from the old samples is down weighted in an
exponentially decaying manner as mentioned before.
1) Foreground Detection: The above operations are
Prior knowledge for multinomial distribution can be
performed and are checked with the learning rate. The
introduced by using its conjugate Prior, the Dirichlet prior learning rate is defined as; when the change of background
given by equation (17).
dynamics becomes smaller the rate for updating the
background model using the current video frame should
(17)
The coefficients cm has a meaningful interpretation. For the decrease. This rate of background model updating is referred
multinomial distribution, the cm presents the prior evidence for to as the learning rate. This is useful in order not to miss any
the class m the number of samples that belong to that class a foreground pixels. When the change of background dynamics
priori. Negative prior evidence means, accept that the class m increase, the learning rate should increase for the background
exists only if there is enough evidence from the data for the model to converge more rapidly in order to reduce the number
existence of this class. This type of prior is also related to of false alarms. Here the learning rate is compared
value, if learning rate exceeds 0.01 then the
minimum message length criterion that is used for selecting with
proper models for given data. The MAP solution that includes pixels are updated to the background model. Then adapted
background model is then taken as the reference for the next
the mentioned prior follows from equation (18).
incoming frames to be compared. This is used for detecting
foreground object which is highly dependent on background
(18)
model. It compares the video frame with the background
Where
frame, and identifies the foreground object from the frame.
Finally, this step eliminates any pixels which are not
We get equation (19)
connected to the image. It involves the process of improving
(19)
the foreground mask based on the information obtained from
Where
the outside background model.
We rewrite (7) as equation (20)
(20)
Where
is the ML estimate from equation
(15) and bias from the prior is introduced through c/t.
The bias decreases for larger data sets (larger t). However,
if a small bias is acceptable we can keep it constant by fixing
c/t to cT= c/T with some large T. This means that the bias will
always be the same as if it would have been for a data set with
T samples. It is easy to show that the recursive version of (18)
with fixed c/t = cT is given by equation (21) .
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C. Geometrical Feature Extraction and Classification
From the detected vehicles by the improved adaptive
Gaussian mixture model [1], the geometrical features like
length, width, area, and perimeter are extracted. The ratios are
calculated for the geometrical features extracted. For each of
the object (vehicle) detected, the reference ratio is calculated
and is taken as RR, for example, lw=length/width;
,al=area/length;
aw=area/width;
ap=area/perimeter;
lp=length/perimeter; wp=width/perimeter and finally produces
the reference ratio as, RR=[lw al aw ap lp wp] and this will be
stored in database as shown in Table 1and Table 2. When the
next vehicle is detected, its ratios are determined say DR.
Then the RR and DR are compared. If both are equal classify
the vehicle type as already detected one else calculate the RR
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 2 – Sep 2014
for the detected vehicle and store in a database for future
reference as shown in Figure.1calculated. By this calculation
the system classify the vehicle as small/heavy. The system
accepts an integer value 0 for light vehicles and 1 for heavy
vehicles types for newly detected vehicles as shown in the
Fig. 3 and Fig. 4.
IV. EXPERIMENTAL RESULTS
The work has been implemented using a set of video inputs,
captured from the immovable camera. The pre-processed
video sequences are given as input to the system. The
improved adaptive Gaussian mixture model approach is
applied so as to achieve the foreground detection. The result
of background modelling is shown in Fig. 2. The detection of
the vehicles is very clear and the frame is not affected by
lighting conditions, clutters and shadow.
Fig. 2 Result of background subtraction (Foreground
Detction)
Once the system receives the video as input to be
processed, the vehicles are detected as the video proceeds.
Firstly in the sample video, a vehicle is detected and the
system interacts with the user to specify the tag (0 for light
vehicles), and after receiving the tag the count of the light
vehicle type is detected and this information is stored to
continue with the count record. With the further proceeding of
the input video, when the new type of the vehicle is detected
subsequently and hence the system interacts with the user and
asks for the new tag for the new type of vehicle category and
the count for the corresponding vehicle types are displayed.
Fig. 3, Fig. 4 and Fig. 5 illustrate the part of results.
Fig. 4 Heavy vehicle detected waiting for vehicle tag option
Fig. 5 Classification result and count of vehicles
The geometrical features extracted are used for the
calculation of reference ratios. The reference ratios are
calculated for each of the vehicle type detected for the first
time and are saved separately for light and heavy vehicles in
text file for future reference as shown in the Table I.
TABLE I
SAMPLE REFERENCE RATIOS (RR) = [LW AL AW AP LP WP] FOR LIGHT AND
HEAVY VEHICLE WHEN DETECTED FOR THE FIRST TIME
Sample
Vehicle
type
lw=Le
ngth/wi
dth
Light
1.7
Heavy
3.8338
al=ar
ea/len
gth
29.13
4
9.216
5
aw=a
rea/
widt
h
ap=a
rea/p
erim
eter
lp=Le
ngth/p
erimet
er
wp=widt
h/perime
ter
59.8
2.14
0.2986
0.17564
35.3
34
3.08
45
0.3346
7
0.08729
6
The determined ratios are calculated by the system and are
compared with the previously stored reference ratio. When the
vehicle is detected it compares with the previously stored
reference ratio (as shown in Table II). If the it is equal, then
system adds the count to the previously detected class type
else it will store the newly calculated ratio as reference ratio
for future comparison and asks the user to tag for the new
class type as 0 or 1 for light and heavy vehicles respectively.
Fig. 3 Light vehicle detected waiting for vehicle tag option
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 2 – Sep 2014
TABLE III
inexpensive to use. Also the system provides an interactive
DETERMINED RATIOS FOR EACH OF THE SAMPLE VEHICLE IMAGES FROM
environment to tag the vehicle types before classification
DIFFERENT FRAMES
through which user can issue new tags for the vehicle type as
lw=L
al=are
aw=a
ap=are
lp=Le
wp=
Us
Vehicl
and when the vehicle is detected. The system can identify new
ength/
a/leng
rea/w
a/peri
ngth/
width
er
e Type
classes for more number of input videos consisting of various
Fram
width
th
idth
meter
perim
/peri
in
e id
eter
meter
pu
vehicles, which is another advantage of the proposed system.
t
It is intended to work on several classification tasks by
tag
1.817
29.59
53.78
0.301
0.165
possible
extensions for the proposed methodology to detect
1
8.9151
0
Light
2
5
1
24
77
more
number
of vehicle classes efficiently with above 90%
3.833
9.216
35.33
0.334
0.087
2
3.0845
1
Heavy
performance.
8
5
4
67
296
3
4
5
6
7
1.817
2
3.833
8
1.817
2
1.817
2
1.817
2
29.59
5
9.216
5
29.59
5
29.59
5
29.59
5
53.78
1
35.33
4
53.78
1
53.78
1
53.78
1
8.9151
3.0845
8.9151
8.9151
8.9151
0.301
24
0.334
67
0.301
24
0.301
24
0.301
24
0.165
77
0.087
296
0.165
77
0.165
77
0.165
77
0
Light
1
Heavy
0
Light
0
Light
0
Light
To analyse the performance of the proposed system the
manually delineated vehicles were used as reference vehicles
to check the accuracy of the system. The performance of the
proposed system is evaluated using the following measures
[12], TPR (true positive (23), false positive (24) and false
negative (25) respectively:
(23)
(24)
(25)
The input videos are taken for testing and results obtained
are shown in Table III. To obtain good performance, the
methodology must try to reduce the false negative rate and
false positive rate types of errors and FNR = 0 and FPR = 0
indicates 100% accuracy of the classification method.
TABLE IIII
PERFORMANCE STATISTICS OF PROPOSED APPROACH
Input
Video
Video 1
Video 2
Video 3
Number of
Vehicles
appearing in
the video
frames
22
52
8
Number
of
vehicles
detected
Number
of false
detection
s
Vehicle
s
missed
18
45
7
0
0
0
04
07
1
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
Accura
cy
%
[8]
[9]
75
86.53
87.5
The sample outcome results signify accuracy and relatively
high level performance of the proposed methodology. It can
detect and classify the vehicles with higher efficiency in the
scenes of traffic without being affected by lighting conditions.
The tagging of the vehicles types, for each of the categories of
the vehicles for sample input video as shown in Fig. 3, Fig. 4
and Fig. 5.
V. CONCLUSION AND FUTURE WORK
The proposed system provides economical and efficient
solution for vehicle detection and classification with the
count. The system uses low level geometrical features like
width, length etc to achieve classification. Hence results in
simple implementation for classification. The system is
ISSN: 2231-5381
ACKNOWLEDGMENT
The authors wish to acknowledge CASIA for providing the
iris database. The shared CASIA Iris Database is available on
the web [10]. The work is partially supported by the Research
Grant from AICTE, Govt. of India, Reference no:
8023/RID/RPS-114(PVT)/2011-12 Dated December,24-2011.
[10]
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