Adaptive structured sub-blocks tracking

Visual object tracking algorithms based on middle level appearance have been widely studied for their effective representation to non-rigid appearance variation and partial occlusion. Sub-blocks are often adopted as local feature in mid-level based tracking algorithms. How to select representative sub-blocks to reveal the spatial structure of objects and retain the flexibility to model non-rigid deformation has not been adequately addressed. Exploiting discrimination, uniqueness and historical prediction accuracy of sub-blocks of a target, we propose a local feature selection method which includes rough initial subblock selection and refined subblock-sample particle bi-directional selection under particle filter tracking framework. A quantitative evaluation is conducted on 10 sequences. Experimental results show the robustness of our proposed algorithm in tackling with non-rigid deformation and partial occlusion.