Background Modeling and Moving Object Detection

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613

Background Modeling and Moving Object Detection Miss. Neha S. Patil1 Dr. S. R. Gengaje2 1 Student 2Head of Dept.

1,2 Department of Electronics Engineering 1,2 WIT, Solapur, India Abstract— The most challenging task for most object detection applications is to accurately segment foreground from the background under the presence of heavy illumination changes. Moving object detection provides the separation of pixels in the foreground and background. While classifying, it is most important to model background for better results as background may contain some inappropriate data like flickering monitors, swaying trees etc. The proposed work uses a texture based method i.e. Local binary pattern for modeling the background and detecting moving objects. This approach is very robust in terms of gray scale variations, as the LBP operator is invariant to monotonic transformation of gray scale. Major steps involved in proposed system are background modeling, background subtraction and then foreground detection. The proposed work is implemented on MATLAB platform.

Key words: Modeling Object Detection, Moving Object Detection I. INTRODUCTION Basically, object detection is a computer technology which is related to computer vision and image processing which deals with detecting single occurrence of semantic objects of a particular class like humans, buildings, cars etc.

Background subtraction and moving object detection has several uses in various fields like traffic monitoring, human action recognition and also many other applications like digital forensics. Basically, background subtraction means subtracting the current image from reference background model.

foreground pixels will also be absorbed into the background model which results in a high false negative rate. This is also known as the foreground aperture problem. Also in areas, such as building gates or highways where people walk, it is most difficult to get good background models without filtering out the effects of foreground objects. A variety of methods have been proposed for moving object detection and many different features are utilized for modeling background. This paper proposes an approach that uses discriminative texture features to capture background statistics. Here LBP operator is used to which has shown excellent performance in many applications.

Texture classification is an active research topic in computer vision. Kashyap and Khotanzad were among the first researchers to study rotation invariant texture classification by using circular autoregressive model [1].

LBP has also been adapted to many other applications, such as face recognition[2], dynamic texture recognition [3] and shape localization [4].LBP has its own properties i.e. it has tolerance against illumination changes and it is computationally very simple. To make LBP more suitable for real world scenes it has been modified and this modified version is used in the proposed method. LBP has a disadvantage in handling moving shadows. And this is the difficult problem to solve with the background modeling. A survey of moving shadow detection is presented in [5].

II. PROBLEM STATEMENT The main objective of the proposed work is to design a framework for modeling the background using texture information operator detecting moving objects in a video sequence.

III. METHODOLOGY

Fig. 1: Generalized Block Diagram of Object Detection There are some challenges in background modeling like swaying trees, weaving water or flickering monitors and also illumination changes. Various techniques have been introduced for background modeling. Each having strength and weaknesses. These background modeling techniques can be classified as basic background modeling, statistical background modeling, fuzzy background modeling. Other classifications also can be found in terms of prediction. The simplest background model assumes that the intensity values of the pixel can be modeled by a single unimodal distribution. However, a single model cannot handle multiple backgrounds like weaving trees. Again, if the model is adapting too quickly then slowly moving

The input will be videos downloaded from internet or recorded videos from CCTV camera. All videos will be in .avi format for simplification. Also the frame rate will be 15-20 fps. Input video is first converted into the frames.

After that LBP operator is applied on every frame for background modeling and then foreground is detected by comparing histograms of the model frame and current frame.

Fig. 2: Block diagram of proposed system This block diagram consists of the following steps:

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Background Modeling and Moving Object Detection (IJSRD/Vol. 4/Issue 05/2016/097)

  

Background modeling Foreground detection Object detection

A. Background Modelling Background modeling is the core component of any object detection technique. The background model should be adaptable to changes in background like illumination or weaving trees etc. Proposed system uses a texture based method for background modeling i.e. local binary pattern. It has several properties that favor its usage in background modeling. It is invariant to the monotonic variations of gray scale. Also there is no need to normalize the input image if we use LBP. This operator labels each pixel of an image region by thresholding the neighborhood of it with the centre value and considering the result as a binary number.

LBP is calculated as,

and false negatives. The number of foreground pixels which are missed are called as the false negatives and the background pixels that are marked as foreground are called as the false positives.

p−1

LBP(P,R) (xc , yc ) = ∑ s(g p − g c )2p

Fig. 3: Result of Object Detection

p=0

Condition, s(x) = 1 for x ≥ 0 and s(x) = 0 for x < 0 LBP histogram is calculated for every block of new video frame. After this the new block histogram say Xt is compared against the k model histograms by using the histogram intersection as the distance measure. If none of the histogram is close enough to the new histogram, then model histogram with lowest weight is replaced with the new histogram and low initial weight is given to it. And if model histogram is close enough to the new histogram is found, then bins of the histogram are updated. And based on these histograms we have to calculate the foreground and background pixels. Histogram intersection is done to find out the common part in two frames.

B. Foreground Detection Foreground detection is done by comparing input frame with the current background model. The areas of the input frame which do not fit to the background model are considered as foreground. So, here new block of histogram Xt is compared against existing B background histograms which are selected at the previous time instant. If match is not found, then the block is considered as concerned to the background. Otherwise it is considered as belonging to the foreground.

C. Object Detection After marking the background and foreground we will simply get the moving object detected. Figure 2 shows the output of proposed system. The histogram of every frame is also calculated for each frame.

IV. RESULTS AND DISCUSSIONS Below figure shows the output of moving object detection using texture based LBP operator. This method belongs to nonparametric methods i.e. no assumptions are needed about the underlying distributions.

While observing the figure we can see that there are some pixels related to foreground are not detected properly. And also we can see that there are some pixels related to the foreground are missing. So the numerical evaluation of this situation is done in terms of false positives

V. CONCLUSION A method for moving object detection is presented, which uses a texture based LBP operator for background modeling.

As it as invariant to monotonic gray scale changes, so the method gives good results in illumination changes occurred in natural scenes.

ACKNOWLEDGEMENT I would like to express my feeling of gratitude towards the number of people who were supportive in successfully making the project. I am also thankful to our project coordinator and project guide Dr. S.R. Gengaje sir for giving valuable suggestions, technical guidance during the project work that I had undertaken. I am thankful to our principal Dr. halkude for the constant encouragement and assistance they provided me.

REFERENCES [1] Marko heikkila and matti pietikainen,”A texture based method for modeling the background and detecting moving objects”IEEE transactions on pattern analysis and machine intelligence, vol.28 no.4, April 2006.

[2] C. stauffer and W.E.L Grimson,”Adaptive background mixture models for real time tracking”,proc. IEEE CS.conf. computer vision and pattern recognition,vol.2,pp.246-252,1999.

[3] T. Matsuyama, T. Ohya, and H. Habe, “Background Subtraction for Non- Stationary Scenes,” Proc. Asian Conf. Computer Vision, pp. 622-667, 2000.

[4] A. Monnet, A. Mittal, N. Paragios, and R. Visvanathan, “Background Modeling and Subtraction of Dynamic Scenes,” Proc. IEEE Int’l Conf. Computer Vision, vol.

2, pp. 1305-1312, 2003.

[5] Q. Zang and R. Klette, “Robust Background Subtraction and Maintenance,” Proc. Int’l Conf. Pattern Recognition, vol. 2, pp. 90-93, 2004.

[6] T. Ojala, M. PietikaÈinen, and D. Harwood, ªA Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, vol. 29, pp. 51-59, 1996.

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