Second-Order Spatial-Temporal Correlation Filters for Visual Tracking

Yufeng Yu, Long Chen, Haoyang He, Jianhui Liu, Weipeng Zhang, Guoxia Xu
2022 Mathematics  
Discriminative correlation filters (DCFs) have been widely used in visual object tracking, but often suffer from two problems: the boundary effect and temporal filtering degradation. To deal with these issues, many DCF-based variants have been proposed and have improved the accuracy of visual object tracking. However, these trackers only adopt first-order data-fitting information and have difficulty maintaining robust tracking in unconstrained scenarios, especially in the case of complex
more » ... nce variations. In this paper, by introducing a second-order data-fitting term to the DCF, we propose a second-order spatial–temporal correlation filter (SSCF) learning model. To be specific, the SSCF tracker both incorporates the first-order and second-order data-fitting terms into the DCF framework and makes the learned correlation filter more discriminative. Meanwhile, the spatial–temporal regularization was integrated to develop a robust model in tracking with complex appearance variations. Extensive experiments were conducted on the benchmarking databases CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M. The results demonstrated that our SSCF can achieve competitive performance compared to the state-of-the-art trackers. When penalty parameter λ was set to 10−5, our SSCF gained DP scores of 0.882, 0.868, 0.706, 0.676, and 0.928 on the CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M databases, respectively.
doi:10.3390/math10050684 fatcat:5aqumuqzfnefrfty3wmmjh64nm