A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems

I recent times quest for development of intelligent security systems has become the need of the hour for making residential and office premises safer. Due to increasing threat from different types of activities leading to breach of security, it has become impossible for conventional security systems to detect such activities and alert security system in advance. Thus, an increasing reliance on surveillance systems has resulted in need for better target detection and tracking techniques. Methods such as Radio Frequency Identification (RFID) tracking are not useful in preventing above mentioned situations; hence there is a need of wide area surveillance. Target tracking via image processing for a video surveillance system provides an attractive solution, which can efficiently track a specific target, record its position throughout the video stream and also analyze its motion pattern. Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are; human-computer interaction [1], security and surveillance [2] [3], video communication and compression [4], augmented reality [5], traffic control [6] [7], medical imaging [8] [9], video editing [10] [11], multimedia contexts [12] [13], complex object movements [14], video streaming [15], healthcare systems and smart indoor security systems. Video tracking is a time consuming process due to the amount of data that is captured and needs to be processed. Further, the algorithm complexity increases if object recognition for tracking is also involved. The objective of video tracking is to maintain detectability of target object in consecutive video frames. To perform video tracking, an algorithm analyses sequential video frames and outputs the movement of targets between the frames. There are a variety of algorithms, each having its strengths and weaknesses. Considering the intended use, it is important to choose the algorithm best suited for the serving the purpose. Traditional tracking algorithms involve foreground extraction of the moving target from a static background and then tracks the coherent blobs of the target. Though, these algorithms are computationally efficient but track all the vehicles which exhibit motion in the stream. Similarly, other tracking algorithms like optical flow techniques as discussed in [16] and wavelet based vehicle tracking as illustrated in [17] also track all the vehicles which exhibit motion or all that are similar in appearance. The lighting condition varies throughout the whole day (depending upon the weather condition for outdoor and lights for indoor). Low light conditions result into poor discrimination of objects from their background and sometimes lighting condition causes shadows or white-out effect. Most of the background subtraction techniques are sensitive to illumination change and it is difficult to handle the shade and shadow caused by the illumination change. Most algorithms which are able to handle these situations, need time on the order of several frames to estimate and train the background model [18]. Less work Keywords