Accurate visual tracking via reliable patch

To tackle the problem that traditional particle-filter- or correlation-filter-based trackers are prone to low tracking accuracy and poor robustness when the target faces challenges such as occlusion, rotation and scale variation in the case of complex scenes, an accurate reliable-patch-based tracker is proposed through exploiting and complementing the advantages of particle filter and correlation filter. Specifically, to cope with the challenge of continuous full occlusion, the target is divided into numerous patches by combining random with hand-crafted partition methods, and then, an effective target position estimation strategy is presented. Subsequently, according to the motion law between the patch and global target in the particle filter framework, two effective resampling rules are designed to remove unreliable particles to avoid tracking drift, and then, the target position can be estimated by the most reliable patches identified. Finally, an effective scale estimation approach is presented, in which the Manhattan distance between the reliable patches is utilized to estimate the target scale, including the target width and height, respectively. Experimental results illustrate that our tracker can not only be robust against the challenges of occlusion, rotation and scale variation, but also outperform state-of-the-art trackers for comparison in overall performance.