Mean-shift-based moving target tracking algorithm in complex industrial environments
Zhongming Liao, Zhaosheng Xu, Xiuhong Xu, Azlan B. Ismail · International Journal of Computer Applications in Technology · 2026
This paper proposes an improved moving Target Tracking Algorithm (TTA) based on the Mean-Shift (MS) method, which is suitable for complex industrial environments.The improved algorithm introduces the You Only Look Once (YOLO) model for moving target detection and uses its results as tracking input.In addition, the algorithm also introduces a twin network (SN) to extract the deep features of the target for re-identification after occlusion.In order to further improve the tracking stability, a Kalman Filter is introduced to predict the next motion state of the target.Stability analysis shows that the algorithm achieves the best Multitarget Tracking Accuracy (MOTA) index in various complex environments, outperforming other tracking methods and showing good multi-target tracking stability.In summary, the algorithm successfully overcomes the limitations of the traditional MS method and provides a novel solution for moving target tracking in industrial environments.The algorithm has important practical value and provides a valuable reference for future research on moving target tracking in dynamic and complex environments.