Improved Circulant Structure with Kernel Tracker using Correlation Kernel based on Wavelet Feature

Visual object tracking has many applications related to computer vision. Recently, correlation filter based trackers have been ranked as the highest performers in this field. However, handling chronic problems such as occlusion, deformation, and scale variations is difficult with such trackers. These problems are solved by many other researches that employ other features and improve an appearance update. In this paper, we propose an improved CSK (Circulant Structure with Kernel) tracker using object feature decomposition in the wavelet domain. Specifically, a newly created correlation kernel is generated from different filter-banks reflecting the visual properties of a given object, and it is stable and robust to the environmental variations. Experimental results demonstrate that the proposed scheme outperforms the conventional CSK tracker in terms of center location error by 59% on an average for 100 sequences. Therefore, we believe that the proposed tracker can be useful for robust object tracking in occlusion.