Wireless Vision Based Object Tracking Using Continuously Adaptive Mean Shift Tracking Algorithm

In this paper we implement a wireless vision based object tracking system with wireless surveillance camera which uses a novel color based object tracking algorithm designed to work on any non-ideal environment. The implementation of the kernel-based tracking of moving video objects based on the CAMSHIFT algorithm is presented. We show that the algorithm performs exceptionally well on moving objects in video sequences and it is robust to changes in shape with complete occlusion. The improvement in performance is achieved by using an adaptive block-based approach for estimating motion between frames and an efficient modulation scheme is used to control the gap between frames used for object tracking. The algorithm for detecting occlusion proceeds in two steps. First, covered regions are estimated from the displaced frame difference. Then, covered regions are classified into actual occlusions and false alarms using motion characteristics. Disocclusion detection is also performed in a similar manner using motion vector information is proposed in this paper.