Abstract
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 appearance 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.
Highlights
Visual object tracking is a fundamental problem in the field of computer vision, which has a wide range of applications in human–computer interaction, video surveillance, unmanned driving, and so on
Extensive experiments on the benchmarking databases demonstrated that our second-order spatial–temporal correlation filter (SSCF) can achieve competitive performance compared to the state-of-the-art trackers
To evaluate the performance of the proposed model, we compared it with the state-of-the-art trackers, including spatially regularized discriminative correlation filters (SRDCFs) [23], kernelized correlation filters (KCFs) [47], spatial–temporal regularized correlation filters (STRCFs) [24], background-aware correlation filters (BACFs) [25], learning adaptive discriminative correlation filters (LADCFs) [26], discriminative scale space tracking (DSST) [48], the scale-adaptive with multiple features tracker (SAMF) [12], ECOHC [49], ARCF-HC [50], the MSCF [51], and AutoTrack [52]
Summary
Visual object tracking is a fundamental problem in the field of computer vision, which has a wide range of applications in human–computer interaction, video surveillance, unmanned driving, and so on. Kumar et al [19] exploited the LBP, color histogram, and pyramid of the histogram of gradients to model the object’s appearance and developed an adaptive multi-cue particle filter method for real-time visual tracking Even though these DCF-based trackers using multi-channel features succeed to some extent, some aspects such as the redundancy of multi-channel features, the boundary effect, and data fitting have not been fully explored. We incorporated the second-order data fitting and spatial–temporal regularization into the DCF framework and developed a more robust tracker; An effective alternating-direction method-of-multipliers (ADMM)-based algorithm was used to solve the proposed tracking model; Extensive experiments on the benchmarking databases demonstrated that our SSCF can achieve competitive performance compared to the state-of-the-art trackers.
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