Scale adaptive part-based tracking method using multiple correlation filters

Visual tracking plays a significant role in computer vision. Although numerous tracking algorithms have shown promising results, target tracking remains a challenging task due to appearance changes caused by deformation, scale variation, and partial occlusion. Part-based methods have great potential in addressing the deformation and partial occlusion issues. Owing to the addition of multiple part trackers, most of these part-based trackers cannot run in real time. Correlation filters have been used in target tracking owing to their high efficiency. However, the correlation filter-based trackers face great problems dealing with occlusion, deformation, and scale variation. To better address the above-mentioned issues, we present a scale adaptive part-based tracking method using multiple correlation filters. Our proposed method utilizes the scale-adaptive tracker for both root and parts. The target location is determined by the responses of root tracker and part trackers collaboratively. To estimate the target scale more precisely, the root scale and each part scale are predicted with the sequential Monte Carlo framework. An adaptive weight joint confidence map is acquired by assigning proper weights to independent confidence maps. Experimental results on the publicly available OTB100 dataset demonstrate that our approach outperforms other state-of-the-art trackers.