Multi-Target Video Tracking Based on Improved Data Association and Mixed Kalman/H∞ Filtering

This paper proposes a novel multi-target video tracking (MTVT) method based on improved data association and mixed Kalman/H-infinity filtering. First, multiple features of video targets, such as local texture, spatial color distribution, and edge-oriented gradients, are extracted to form the fused-feature matching matrix. Second, an efficient and accurate video targets association method integrating an improved probabilistic data association (IPDA) and a simplified joint probabilistic data association (SJPDA) is developed. The IPDA combines the augmented posterior probability matrix with the fused-feature matching matrix for a multitarget association. On the other hand, the SJPDA ensures the efficiency of data association, and better accuracy in the presence of low peak signal-tonoise ratio and sparse target environment by sifting out big probability events. Finally, a mixed Kalman/H-infinity filtering fusing the covariances of state estimation is proposed for the fast and robust state estimation of video targets. Experimental results demonstrate that the proposed multifeature fusion MTVT is able to describe video targets accurately, guarantee both the efficiency and accuracy in the video targets association, and also it is more reliable and precise under noisy environments.