Robust object tracking via multi-scale patch based sparse coding histogram

There are many visual tracking algorithms that are based on sparse representation appearance model. Most of them are modeled by local patches with fixed patch scale, which make trackers less effective when objects undergone appearance changes such as illumination variation, pose change or partial occlusion. To solve the problem, a novel appearance representation model is proposed via multi-scale patch based sparse coding histogram for robust visual tracking. In this paper, the appearance of an object is modeled by different scale patches, which are represented by sparse coding histogram with different scale dictionaries. Then a similarity measure is applied to the calculation of the distance between the sparse coding histograms of target candidate and target template. Finally, the similarity score of the target candidate is passed to a particle filter to estimate the target state sequentially in the tracking process. Additionally, in order to decrease the visual drift caused by partial occlusion, an occlusion handling strategy is adopted, which takes the spatial information of multi-scale patches and occlusion into account. Based on the experimental results on some benchmarks of video sequences, our tracker outperforms state-of-the-art tracking methods.