Ruoling Yang, Tingfa Xu, Yunru Bai, Axin Fan, Bo Yuan
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Multi-scale Correlation Filter Tracking with Adaptive Re-detection Mechanism
Tracking is a popular research topic in artificial intelligence, but how to handle the severe occlusion and deformation remains a challenging problem. Focusing on this issue, we propose a multi-scale correlation filter tracker using a re-detection module. Specifically, we utilize a reliable confidence strategy to estimate the reliability of initial results, and introduce a novel template matching technique to solve the target relocation problem. Experiment results demonstrate that our method can outperform several classic trackers.
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