Object Tracking with Blocked Color Histogram

Xiaoyu Chen, Lianfa Bai, Yi Zhang, Jing Han

Abstract: Object deformation and blur are challenging problems in visual object tracking. Most existing methods increase the generalization of the features to decrease the sensitivity of spatial structure or combine statistical feature and spatial structure feature. This paper presents a novel approach to add structure characteristics to color histograms with blocked color histogram (BCH) to increase the robustness of trackers based on color histogram especially in deformation or blur problems. The proposed approach works by computing color histograms of every blocks extracted from given boxes. We strengthen structure characteristics by separating the whole box to several parts and use the color histogram of the individual parts to track, then weighting the results, and the result shows that this improves the performance compared to the methods using the whole color histogram. We also use double layer structure to speed up the method with the necessary accuracy. The proposed method gets good score in VOT2015 and VOT2016.