Patchwise Tracking via Spatio-Temporal Constraint-Based Sparse Representation and Multiple-Instance Learning-Based SVM

  • Yuxia WangEmail author
  • Qingjie Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)


This paper proposes a patch-based tracking algorithm via a hybrid generative-discriminative appearance model. For establishing the generative appearance model, we present a spatio-temporal constraint-based sparse representation (STSR), which not only exploits the intrinsic relationship among the target candidates and the spatial layout of the patches inside each candidate, but also preserves the temporal similarity in consecutive frames. To construct the discriminative appearance model, we utilize the multiple-instance learning-based support vector machine (MIL&SVM), which is robust to occlusion and alleviates the drifting problem. According to the classification result, the occlusion state can be predicted, and it is further used in the templates updating, making the templates more efficient both for the generative and discriminative model. Finally, we incorporate the hybrid appearance model into a particle filter framework. Experimental results on six challenging sequences demonstrate that our tracker is robust in dealing with occlusion.


Patchwise tracking Hybrid generative-discriminative appearance model MIL&SVM Spatio-temporal constraint Sparse representation 



This work is supported by the National Natural Science Foundation of China (No. 61175096 and 61273273), Specialized Fund for Joint Building Program of Beijing municipal Education Commission.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Beijing Lab of Intelligent Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingPeople’s Republic of China

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