Distributed Multi-Target Tracking Based on the K-MTSCF Algorithm in Camera Networks

It is a challenging task to develop an effective multi-target tracking algorithm for camera networks due to the factors, such as spurious measurement, limited field of view, complexity of data association algorithm, and so on. In a real-life environment, the system model for camera networks is usually nonlinear, and hence, a Kalman filter may be inappropriate for modeling this system. Besides, the main drawback of traditional joint probabilistic data association (JPDA) is prone to raise the combinatorial explosion problem when the association probabilities have to be calculated. To solve these problems, a multi-target square-root cubature information weighted consensus filter (MTSCF) combined with a K-best joint probabilistic data association algorithm is proposed in this paper. The proposed K-MTSCF algorithm can not only reduce the effect of data association uncertainty stemming from the ambiguity of measurements and computation complexity of data association algorithm by K-best JPDA, but also increase tracking accuracy and stability using MTSCF algorithm. The experimental results demonstrate that the proposed approach performs favorably against the state-of-the-art methods in terms of accuracy and stability for tracking multiple targets in camera networks.