Article: Mean-shift-based moving target tracking algorithm in complex industrial environments Journal: International Journal of Computer Applications in Technology (IJCAT) 2026 Vol.78 No.2 pp.112 - 123 Abstract: 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 Multi-target 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. Inderscience Publishers - linking academia, business and industry through research

Title: Mean-shift-based moving target tracking algorithm in complex industrial environments

Authors: Zhongming Liao; Zhaosheng Xu; Xiuhong Xu; Azlan Ismail

Addresses: School of Mathematics and Computer Science, Xinyu College, Xinyu, Jiangxi, China; College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia ' School of Mathematics and Computer Science, Xinyu College, Xinyu, Jiangxi, China; College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia ' School of Vocational and Continuing Education, Xinyu College, Xinyu, Jiangxi, China ' College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi (UiTM), Shah Alam, Selangor, Malaysia

Abstract: 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 Multi-target 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.

Keywords: moving target tracking; mean-shift algorithm; YOLO model; Siamese network; Kalman Filter.

DOI: 10.1504/IJCAT.2026.151718

International Journal of Computer Applications in Technology, 2026 Vol.78 No.2, pp.112 - 123

Received: 26 Feb 2025
Accepted: 18 Sep 2025
Published online: 17 Feb 2026 *

Title: Mean-shift-based moving target tracking algorithm in complex industrial environments

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