Sparse Similarity Learning with a Local Appearance Model for Person Tracking

Xiang Li, Min Xu, Zeqiang Wei, Liming Shao

Abstract: In this paper, we propose a novel approach to robust person tracking that combines an online bilinear similarity metric learning with a local appearance model in particle filter framework. Due to various appearance and motion changes of the target person in challenging scenarios, conventional pre-defined similarity metrics are prone to drifting in dealing with challenging sequences. To this end, we propose to learn a discriminative metric to distinguish the target object from the background by using a sparse online bilinear similarity function parameterized by a diagonal matrix. In addition, most metric learning based appearance models only consider the holistic representation and hence are sensitive to partial occlusion and cluttered background. To address this issue, we employ a local appearance model and a simple template update strategy to build a robust person tracker. Experimental results on several challenging person videos show that our tracker achieves superior performance to several state-of-the-art trackers.