Particle filter-based multitarget multicamera tracking system utilizing random finite sets and distributed estimation process

Abstract

Tracking any moving object is certainly a challenging task. It becomes considerably more complicated as the number of object increases. For such a system, the utilization of some sensors working collaboratively will give a distinct advantage. In the work presented here, the problem of a visual tracking system developed for a multitarget multicamera environment, is considered and a solution based on joint algorithm between random set theory and distributed estimation process is proposed. The complexity of an object tracking system emerges from the uncertainties present in the system, such as noise, clutter, occlusion, ambiguous object movement, changes of object appearance, etc. In order to handle these hurdles, particle filter algorithm based on recursive Bayesian filtering approach is employed to perform the logical inferencing process, and random set theory is used to manage the multitarget nature of the system, while distributed computation mechanism is applied to handle many sensors operated in the area of investigation. The performance of proposed algorithm is examined using real video data captured from two UAVs (Unmanned Aerial Vehicles) flown at 52 m height. The results show that the tracking system successfully detects and tracks the targets.