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

• Published in: Guang Dian Gong Cheng = Opto-Electronic Engineering 2021-01, Vol.48 (1), p.200068
• Main Authors: Wang, Ye, Liu, Qiang, Qin Linbo, Teng Qizhi, He Xiaohai
• Format: Article
• Language: chi ; eng
• Subjects: Adaptive algorithms; Adaptive filters; Background noise; Correlation; Discrimination; Feature extraction; International conferences; Occlusion; Optical tracking; Pattern recognition; Regularization
• ISSN: 1003-501X
• DOI: 10.12086/oee.2021.200068

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