Particle PHD filter based multi-target tracking using discriminative group-structured dictionary learning

Abstract

Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propose a novel video-based multi-target tracking system that combines the particle probability hypothesis density (PHD) filter with discriminative group-structured dictionary learning. The discriminative dictionary with group structure learned by the hierarchical K-means clustering algorithm implicitly associates the dictionary atoms with the group labels, simultaneously enforcing the target candidates from the same group (class) to share the same structured sparsity pattern. Furthermore, we propose a new joint likelihood calculation by relating the discriminative sparse codes with the maximum voting technique to enhance the particle PHD updating step. Experimental results on two publicly available benchmark video sequences confirm the improved performance of our proposed method over other state-of-the-art techniques in video-based multi-target tracking.