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

Abstract

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.