Online multi-object tracking based on hierarchical association and sparse representation
Zijian Lin, Huicheng Zheng, Bo Ke, Lvran Chen · 2017
In recent years, sparse representation has been applied to multi-object tracking and shows promising performance. But existing methods often lead to considerable computation. In this paper, we propose a two-level hierarchical association approach to improve the accuracy and efficiency of online multi-object tracker based on sparse representation. We employ a time-saving affinity measure and a discriminative sparse representation classifier to handle objects with disparate and similar appearances, respectively. We also propose a novel strategy for track termination to protect the reliable tracks containing more detections and restrain the unreliable tracks at the same time. Experimental results demonstrate that the proposed method outperforms state-of-the-art online methods.