Clustering based ensemble correlation tracking

Correlation filter based tracking has attracted many researchers' attention in the recent years for its high efficiency and robustness. Most existing work has focused on exploiting different characteristics with correlation filter for visual tracking, e.g., circulant structure, kernel trick, effective feature representation and context information. Despite much success having been demonstrated, numerous issues remain to be addressed. Firstly, the target appearance model can not precisely represent the target in the tracking process because of the influence of scale variation. Secondly, online correlation tracking algorithms often encounter the model drift problem. In this paper, we propose a clustering based ensemble correlation tracker to deal with the above problems. Specifically, we extend the tracking correlation filter by embedding a scale factor into the kernelized matrix to handle the scale variation. Furthermore, a novel non-parametric sequential clustering method is proposed for efficiently mining the low rank structure of historical object representation through weighted cluster centers. Moreover, to alleviate the model drift, an object spatial distribution is obtained by matching the adaptive object template learned from the cluster centers. Similar to a coarse-to-fine search strategy, the spatial distribution is not only used for providing weakly supervised information, but also adopted to reduce the computational complexity in the detection procedure which can alleviate the model drift problem effectively. In this way, the proposed approach could estimate the object state accurately. Extensive experiments show the superiority of the proposed method.