Penalized Gaussian mixture probability hypothesis density tracker with multi-feature fusion
Xiaolong Zhou, Yazhe Tang, Jianyu Yang, Zhen Hua Xie, Shengyong Chen · 2014
This paper presents a penalized Gaussian mixture probability hypothesis density tracker with multi-feature fusion to track close moving targets in video. A weight matrix that contains all updated weights between the predicted target states and the measurements is first constructed. The ambiguous weights in the constructed weight matrix is then determined according to the total weight and the predicted target states. Multiple features such as spatial-color appearance, histogram of oriented gradient, and target area are fused to further penalize the ambiguous weights. The experimental results conducted on both of the synthetical and real videos validate the effectiveness of the proposed tracker.