Feature adaptation-based multipeak-redetection spatial-aware correlation filter for object tracking

摘要

Visual tracking has always been an important research topic in the field of computer vision. During tracking, cluttered backgrounds, deformations, and occasional occlusion inevitably cause unpredictable appearance changes in the target, making the task very challenging. Traditional methods often tackle this problem using fixed central region constraints, simple combinations of features and independent classifiers. However, existing constraints are not very accurate; the simple mechanism of combining features lacks descriptive robustness, and the independent classifiers require complex additional training. Therefore, in this paper, we propose a correlation filter tracker called the feature adaptation-based multipeak-redetection spatial-aware correlation filter (FMCF) to accurately track targets that often change in appearance. In this tracker, the correlation filter has a dynamic spatial constraint that effectively reduces the response of the filter to the cluttered background area. To address target deformation, a new feature adaptation-based training method is designed, and the final response is a combination of the correlation filter and an independent statistical color model. Finally, a multipeak-redetection scheme, which can find the missing targets within multiple peaks of the final response map, is proposed to handle occlusion. In particular, the statistical color model is employed in the spatial-aware filter construction, the final response calculation, and the multipeak-redetection procedure to improve the robustness of the tracker, because of its ability to generate pixel-level color probabilities. Extensive experimental results on public tracking datasets prove that our proposed tracker can perform better than state-ofthe-art trackers. Moreover, compared to the baseline tracker, the precision rate and success rate of FMCF are increased by 6.5 and 4.8 on the OTB100 benchmark and by 12.7 and 8.2 on the TC128 benchmark, respectively.(c) 2022 Elsevier B.V. All rights reserved.