Verified Background-Aware Filters and Re-detection Tracking Algorithms

In order to overcome the problem of the the tracking accuracy of the correlation filter is reduced when the target is occluded. We propose a validated background-aware filters and re-detection tracking algorithm to improve the robustness of target tracking. Firstly, the maximum response value and the average peak-to correlation energy are applied to judge the occlusion degree of the predicted target with the background-aware filters. Then, we used the high-confidence target that matched by SiamFC to replace the occluded target, or discarded to update the model of the low-confidence targets. The TB-50 Benchmark is used to compare the experimental results. Our proposed algorithm can obtain the success rate of 55.7% and the accuracy of 78.0%. Compared with the background-aware correlation filters algorithm, the proposed algorithm improves 4.3% and 1.1% respectively. By updating the target model in terms of validation and re-detection, the background-aware filters algorithm can achieve higher tracking success rate and accuracy. In general, the proposed algorithm shows more stable and accurate tracking performance in the case of occlusion, fast moving, background clutter, etc.

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