A non-parametric sequential clustering is proposed for efficiently mining the low rank structure of historical objects represented by weighted cluster centers.

To alleviate model drift, an adaptive object template is learned by the weighted clustered centers which can be used to calculate the spatial distribution of object and provide weakly supervised information for re-correcting the object state.

A clustering based ensemble correlation tracker is proposed to jointly capture the target appearance by multi-scale kernelized correlation filter and exploit the long-term object properties by the object template with cluster analysis.