Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (6): 1417-1428.DOI: 10.3778/j.issn.1673-9418.2011057

• Graphics and Image • Previous Articles     Next Articles

Object Tracking Algorithm with Fusion of Multi-feature and Channel Awareness

ZHAO Yunji, FAN Cunliang(), ZHANG Xinliang   

  1. College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, Henan 454003, China
  • Received:2020-11-20 Revised:2021-02-05 Online:2022-06-01 Published:2021-03-08
  • About author:ZHAO Yunji, born in 1980, Ph.D., lecturer. His research interests include pattern recognition and intelligent control.
    FAN Cunliang, born in 1995, M.S. candidate. His research interests include pattern recognition and intelligent control.
    ZHANG Xinliang, born in 1978, Ph.D., associate professor. His research interests include detection technology and automation equipment.
  • Supported by:
    National Natural Science Foundation of China(U1504506);Key Technologies Research and Development Program of Henan Province(192102210073);Foundation for University Key Teachers from Henan Province(2017GGJS051);Fundamental Research Funds for the Universities of Henan Province(NSFRF200310)


赵运基, 范存良(), 张新良   

  1. 河南理工大学 电气工程与自动化学院,河南 焦作 454003
  • 通讯作者: + E-mail:
  • 作者简介:赵运基(1980—),男,河南南阳人,博士,讲师,主要研究方向为模式识别、智能控制。
  • 基金资助:


In order to solve the problem of drift or overfitting in the tracking process of depth feature description target, an object tracking algorithm combining multiple features and channel perception is proposed. The depth feature of the tracking target is extracted by the pre-training model, the correlation filter is built according to the feature, and the weight coefficient of each channel filter is calculated. According to the weight coefficient, the feature channel generated by the pre-training model is screened. The standard deviation of the retained features is calculated to generate statistical features and they are fused with the original features. The fused features are used to construct related filters and correlation operations are performed to obtain feature response maps to determine the location and scale of the target. Based on the depth feature of the tracking result area, the filter constructed by fusion feature is made sparse online updates. The algorithm in this paper and some current mainstream tracking algorithms are tested on the public datasets OTB100, VOT2015 and VOT2016. Compared with UDT, without affecting the tracking speed, the proposed algorithm has stronger robustness and higher tracking accuracy. The experimental results show that the proposed algorithm shows strong robustness under the challenges of target scale variation, fast motion and background clutters.

Key words: object tracking, depth feature, channel screening, feature fusion, sparse update



关键词: 目标跟踪, 深度特征, 通道筛选, 特征融合, 稀疏更新

CLC Number: