Abstract Background subtraction is a method to identify object and famous used in moving object detection. The aim is to obtain a clean background and then detect moving objects by comparing it with the current frame. In this paper, kalman filter method and Frame difference has used to detect the objects. The kalman filter has better accuracy than the Frame difference method. Experimental results show that the kalman filter is a good solution to obtain high accuracy, low resource requirements in given video of each technique. The detection of object will be shown in the result

C Srinivas Rao

P Darwin

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International Journal of Multidisciplinary Sciences and Engineering Research   
  
 ©2014 IJMSER, All Rights Reserved 
Available at http://www.ijmser.com/ 
 
22| Int. J. of Multidisciplinary Sciences and Engineering Research, Vol.1, No.1 (April 2014) 
 
RESEARCH ARTICLE  
Frame Difference and Kalman Filter Techniques for Detection of Moving 
Vehicles in Video Surveillance  
 
C.Srinivas Rao
1
and  P.Darwin
2 
 
1M.tech.Student, Godavari institute of ENG & Technology, Rajahmundry 
2Associate Professor, Godavari institute of ENG & Technology, Rajahmundry 
 
Accepted 05 March 2014, Available online 01 April 2014, Vol.1, No.1 (April 2014) 
 
 
Abstract  
  
Background subtraction is a method to identify object and famous used in moving object detection. The aim is to obtain a 
clean background and then detect moving objects by comparing it with the current frame. In this paper, kalman filter 
method and Frame difference has used to detect the objects. The kalman filter has better accuracy than the Frame 
difference method. Experimental results show that the kalman filter is a good solution to obtain high accuracy, low 
resource requirements in given video of each technique. The detection of object will be shown in the result. 
 
Keywords: Visual Surveillance, Background Subtraction, kalman filter, Frame difference s  
 
 
1. Introduction 
 
Nowadays, vision sensor has been introduced in detecting 
object in day time or night time. Vision sensor can be 
applied in many areas, such as in medical system, military 
system, transportation system, robotic and control system, 
and surveillance system. Using vision, the system can 
detect, recognize and actuate accurately depend on how 
good the image have been processing [1],[2],[3],[4]. 
 Background subtraction is a low-level task, it should 
consider two aspects: accuracy and computational 
resources (time and memory). Background subtraction 
performance depends mainly on the background modeling 
technique [8]. Extensive research has been carried out 
regarding this task [5]-[7], [8], [9].  
 In the past, a frame difference using online K-means 
approximation is one of the most popular methods. In this 
technique, computation cost and memory requirements are 
low. However, the foreground segmentation accuracy 
inevitably decreases due to the loss of color information. 
To solve the problem of accuracy and resource 
requirements, this paper proposes a background 
subtraction method by kalman filter method.It is working 
on dynamic and static videos. In this paper, we use static 
videos. 
 In this paper is organized as follows. Frame difference 
method in section II. Kalman filter in section III.The 
simulation results are presented in Section Iv. Concluding 
remarks are made in Section V. 
 
2. Frame Difference 
Background subtraction as the name suggest is the process 
of separating out foreground objects from the background 
in a sequence of video frames . Many methods exist for 
background subtraction, each with different strength and 
weakness in terms of performance and computational 
requirements. Background subtraction is implemented 
using varying complexity.  
1)  Low complexity-using frame difference method.  
2) Medium complexity using approximate median method  
3) High complexity using mixture of Gaussian method. 
 
 
Fig.1. Algorithm of the frame difference algorithm 
C.Srinivas Rao et al                                          Frame Difference and Kalman Filter Techniques for Detection of Moving Vehicles in Video Surveillance 
 
23 | Int. J. of Multidisciplinary Sciences and Engineering Research, Vol.1, No.1 (April 2014) 
 
The algorithm is relatively simple.   
1. Convert the incoming frame „fr‟ to grayscale (here we 
assume a color RGB sensor) 
2. Subtract the current frame from the background 
model „bg_bw‟ (in this case it‟s just the previous 
frame) 
3. For each pixel, if the difference between the current 
frame and backround „fr_diff (j,k)‟ is greater than a 
threshold „thresh‟, the pixel is considered part of the 
foreground. 
The frame difference technique is low accuracy and it 
cannot detect the moving vehicle correctly as shown in 
fig.(2). 
 
3. Kalman Filter 
 
Kalman filters have also been used to perform foreground 
object detection. In each pixel is modeled using a Kalman 
filter. In, a robust Kalman filter is used in tracking explicit 
curves. A robust Kalman filter framework for the recovery 
of moving objects‟. However, the framework does not 
model dynamic, textured backgrounds. Kalman filter also 
work on dynamic videos. 
The algorithm of kalman filter as shown below 
Read the vehicle video 
Convert video in to frames i.e I(x,y) 
For k=1:totalframes 
Ik=FkIk-1+BkUk                                                     (3) 
Pk=FkPk-1F
T
k+Qk                                                      (4) 
Yk=zk-HkIk                                                          (5) 
Sk=HkPkH
T
k+Rk                                                 (6) 
Kk=PkH
T
kSk
-1                                                                            
(8) 
Ik=Ik-1+KkYk                                                           (9) 
End 
Presuming a very small process variance, we let Q=1e-5. 
(We could certainly let Q=0  but assuming a small but 
non-zero value gives us more flexibility in “tuning” the 
filter as we will demonstrate below.) Let‟s assume that 
from experience we know that the true value of the 
random constant has a standard normal probability 
distribution, so we will “seed” our filter with the guess that 
the constant is 0.  
Ik and zk are the actual state and measurement vectors. Ik 
and zk are the approximate state and measurement vectors 
from eq (3) and (5). 
H is the Jacobian matrix of partial derivatives of  h  with 
respect to x. 
                  (10) 
 
The image Ik, represents the estimation of image at frame 
k given observation up to and including at frame k .Pk is 
the error covariance matrix that is measure of the estimate 
accuracy of the state estimate. In the update phase, the 
current prediction is combined with current observation 
information to update the state estimate. Typically, the two 
phases alternate, with the prediction advancing the state 
until the next scheduled observation, and the update 
incorporating the observation. However, this is not 
necessary; if an observation is unavailable for some 
reason, the update may be skipped and multiple prediction 
steps performed. Likewise, if multiple independent 
observations are available at the same time, multiple 
update steps may be performed typically with different 
observation matrices Hk . The formula for the updated 
estimate and covariance above is only valid for the optimal 
Kalman gain. 
 
4. Experimental Results 
 
In this paper, the experiments can done using MALAB 7.8 
in a 2.8 GHz Intel Dual core 860 CPU. The grayscale 
images are generated from the original color images. 
Secondly, Kalman filter images are generated from the 
original color images.  
 Figure 1 shows the original image in color image. The 
comparison between frame difference method and kalman 
filter, kalman filter give better detection. The frame 
difference more noise and if we do the extraction of 
image, it makes the extraction lousier. The number of 
vehicles in given video is 11. 
 In Figure 2, shows the detection object using Kalman 
filtering method. The image is smooth, clean and the 
object is clearly detection. The number of vehicles in 
given video is 9.In fig.1 big noises are considered as on 
vehicle. This is the drawback of Gaussian method. 
 
 
Fig.1. original frame 
 
 
 
Fig.2. object detection using frame difference  methd 
C.Srinivas Rao et al                                          Frame Difference and Kalman Filter Techniques for Detection of Moving Vehicles in Video Surveillance 
 
24 | Int. J. of Multidisciplinary Sciences and Engineering Research, Vol.1, No.1 (April 2014) 
 
 
Fig.3. original frame 
 
 
 
Fig.4. object detection using kalman filter method 
 
 
Conclusion 
 
In this paper, kalman filter has better accuracy compare to 
frame difference method. Furthermore the segmentation 
has been improve and the object detection more smooth. 
In future scope is to detect objects using practical filter. 
 
References 
 
[1]  Ciaran O Conaire, Noel O‟ Connnor, Eddie Cooke, 
Alan Smeaton. “Detection Thresholding Using Mutual 
Information”.  International Conference on Computer 
Vision Theory and Applications, 25-28 February 2006, 
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[2]  Weiming Hu, Tieniu Tan, Liang Wang, Steve 
Maybank. “A Survey on Visual Surveillance of Object 
Motion and Behaviors”.  IEEE Transactions on Systems, 
Man, and Cybernatics, No. 3, August 2004.  
[3]  Attila Jozsef Kun, Zoltan Vamossy. “Traffic 
Monitoring with Computer Vision”. 7th International 
Symposium on Applied Machine Intelligence and 
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[4]  Jianping Wu, Zhaobin Liu, Jinxiang Li, Caidong Gu, 
Maoxin Si, Fangyong Tan. “An Algorithm for Automatic 
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[5] M. Piccardi, “Background subtraction techniques: a 
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[6]  R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, 
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[7]  Y. Benezeth, P. M. Jodoin, B. Emile, H. Laurent, and 
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Frame Difference And Kalman Filter Techniques For Detection Of Moving Vehicles

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Frame Difference And Kalman Filter Techniques For Detection Of Moving Vehicles

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