Optimized Algorithm for Target Tracking using Classification Information based Association Filter

Abstract

In last few decades, multiple target tracking fetches quite attention to the researchers for object localization and monitoring target trajectories which has become one of the most used technique in the area of visual tracking, traffic monitoring, air surveillance system, robotics and vision. On the basis of S-D assignment algorithm, a new algorithm for tracking multiple targets in presence of clutter is designed. By considering target classification information received as special feature from target scan report, cost coefficients of dynamic assignment matrix are modified accordingly using joint probabilistic data association filter. The tracking results get improved with the use of target class and kinematic features information where the association costs are similar for different targets. With the help of the information collected in current scan the classifier output is dynamically updated to incorporate new target classes to be used future scans. Simulation results show that new algorithm can attain competitive tracking performance with distributed computational load by utilizing target classification information into dynamic multidimensional assignment algorithm. The main contribution of this paper is the development of new target tracking method based on IMM filter which generate dynamic classifier to incorporate target features information. This additional information about targets present in current scan helps to take future scans data association decisions.