Learning adaptive spatial regularization and aberrance repression correlation filters for visual tracking

This paper proposes a correlation filter tracking algorithm based on adaptive spatial regularization and aberrance repression aiming at the problem that the spatial regularization weight of the background-aware correlation filter is fixed and does not adapt to the change of the target, and the problem that enlarging search area may introduce background noise, decreasing the discrimination ability of filters. First, FHOG features, CN features, and gray features are extracted to enhance the algorithm's ability to express the target. Second, aberrance repression terms are added to the target function to constrain the response map of the current frame, and to enhance the filter's discrimination ability to alleviate the filter model degradation. Finally, adaptive spatial regularization terms are added to the objective function to make the spatial regularization weights being updated as the objective changes, so that the filter can make full use of the target's diversity information. This paper involves experiments on the public data sets OTB-2013, OTB-2015 and VOT2016 to evaluate the proposed algorithm. The experimental results show that the speed of the algorithm used in this paper is 20 frames/s, evaluation indicators such as distance accuracy and success rate are superior to comparison algorithms, and it has good robustness in a variety of complex scenarios such as occlusion, background interference, and rotation changes.