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Target tracking in computer vision is a challenging task that requires both accuracy and robustness, especially in the presence of occlusions and complex backgrounds. To tackle these challenges, this paper proposes a novel tracking approach based on correlation filters, which incorporates reliability assessment and re-detection modules. The proposed approach leverages enhanced color histogram scores in combination with correlation filter response maps to improve the accuracy of target localization. By considering metrics such as maximum response value, average peak-to-correlation energy, and peak-to-sidelobe ratio, the reliability of the tracking process is evaluated, providing insights into its performance and stability. In cases of tracking failure, a re-detection module is activated to reacquire the lost target and resume the tracking task. This module dynamically adjusts and updates the model to enhance the accuracy and robustness of the tracking process. Extensive experiments are conducted on popular benchmark datasets includingOTB50, OTB100, as well as custom datasets. The results demonstrate the superiority of the proposed approach, particularly in challenging scenarios with occlusions and complex scenes, outperforming baseline trackers and other state-of-the-art tracking methods.
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