Visual Tracking via Adaptive Random Projection Based on Sub-Regions

This paper aims to track arbitrary single target object in a video sequence given its location in the first frame and no other information. In order to track the location and further reduce the influence of occlusion, a part-based appearance model is constructed with color, texture, and spatial structure features extracted in the compressed domain. Moreover, the confidence distributions of different sub-regions bring rich information of appearance change, which enables us to duly update the classifier parameters and to further improve the robustness and stability. In order to reduce the appearance change caused by scale interference, median flow tracking is employed to estimate the scale variation among consecutive frames. Extensive evaluations on challenging benchmark video sequences demonstrate that the proposed tracking algorithm outperforms several state-of-the-art methods in terms of success, precision, robustness, and stability.