Joint Correlation Filtering for Visual Tracking

Correlation filtering-based visual tracking has achieved impressive success in terms of both tracking accuracy and computational efficiency. In this paper, a novel correlation filtering approach is proposed by means of joint learning to bridge the gap between the circulant filtering and the classical filtering methods. The circulant structure of tracking and the information from successive frames are simultaneously exploited in the proposed work. A new formulation for the correlation filter learning is proposed to enhance the discrimination of the learned filter by integrating both the kernel and the image feature domains. The proposed approach is computationally efficient since a closed-form solution is derived for the new formulation. Extensive experiments are conducted on two popular tracking benchmarks, and the experimental results demonstrate that the proposed tracker outperforms most of the state-of-the-art trackers.