Target tracking algorithm based on multi-target features

Accurately tracking dynamic objects in video sequences is an ongoing challenge in target tracking tasks, especially in the face of occlusion, fast motion, and drastic changes in target scale and appearance. Although significant progress has been made in deep learning-based target tracking algorithms, these approaches often overlook the potential value of target history feature information in enhancing tracking stability and accuracy. To this end, we propose a novel target tracking framework that enhances the performance of the tracking algorithms by integrating both long-term and short-term historical feature information of the target into the tracking algorithms. This structure not only simplifies the processing flow, but also dramatically improves the operational efficiency through parallel processing mechanisms, enabling the algorithm to achieve fast and accurate target tracking in complex dynamic environments. Experimental results on several publicly available target tracking datasets show that our approach provides significant improvements in improving the accuracy and robustness of tracking compared to existing algorithms.