Anti-occlusion correlation filter tracking method from a convolution perspective

Because the current correlation filter tracking algorithm is prone to tracking drift or even loss when the target rotates, moves fast, and is occluded, an anti-occlusion correlation filter tracking method is proposed under a convoluted perspective. Based on the framework of the correlation filtering algorithm, the method uses context-aware methods to increase background information. It introduces a multimodal history pool update strategy to enhance the anti-occlusion tracking performance. First, a formula derivation system based on the convolution perspective is designed, and the convoluted theorem is subtly introduced to solve the filter in the frequency domain. Compared with the filter solution method of circulant matrix diagonalization in the existing literature, the method is easy to understand. Then, by submitting context-related information, reasonable energy functional is designed to suppress the response value of the background region to achieve the purpose of tracking the target more robustly. Finally, a historical multimodal target pool is established. When the similarity between the sample with a significant relevant response and each multimodal template in the historical template pool is lower than the artificially set threshold, it is determined that the frame is occluded. In this situation, the template pool, appearance model and filter should not be updated, effectively solving the challenge of occlusion and track drift issues. The proposed method was tested on OTB2015. Experiments show that under the conditions of target rotation, fast movement, occlusion, etc., the proposed method can ensure accurate tracking while maintaining a high speed, which is better than other methods proposed in the experiment.