Robust object tracking with background-weighted local kernels

Object tracking is critical to Visual surveillance, activity analysis and event/gesture recognition. The major issues to be addressed in visual tracking are illumination changes, occlusion, appearance and scale variations. In this paper, we propose a weighted fragment based approach that tackles partial occlusion. The weights are derived from the difference between the fragment and background colors. Further, a fast and yet stable model updation method is described. We also demonstrate how edge information can be merged into the mean shift framework Without having to Use a joint histogram. This is used for tracking objects of varying sizes. Ideas presented here are computationally simple enough to be executed in real-time and can be directly extended to a Multiple object tracking system. (c) 2008 Elsevier Inc. All rights reserved.