Research on ECO-HC Target Tracking Algorithm Based on Adaptive Template Update and Multi-Feature Fusion

Aiming at the problem that it is difficult to fully and effectively utilize features for complete representation of target information during target tracking in complex scenes, this paper proposes an ECO-HC target tracking algorithm based on adaptive template updating and multi-feature fusion. In terms of template updating, an adaptive template mechanism is introduced, which dynamically determines whether the target is occluded or deformed by fusing color correlation and hash correlation, thus adjusting the template updating strategy to avoid template contamination. In terms of feature extraction, average filtered local binary pattern (AVLBP) features are proposed and fused with HOG features to enhance the algorithm's ability to capture the detailed texture of the target. Experiments on the OTB-100 tracking datasets show that the accuracy and success rate of this paper's algorithm on the datasets are 86.7% and 79.7%, respectively, which are improved by 2.9% and 2.7%, respectively, compared to the baseline method. The effectiveness of the algorithm is verified through qualitative and quantitative analysis, which demonstrates that the adaptive template updating and multi-feature fusion strategies can significantly improve the robustness and accuracy of target tracking.