Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 24-40.DOI: 10.3778/j.issn.1002-8331.2107-0337

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Review on Object Tracking Methods for Restricted Computing Resources

WU Zhewei, ZHOU Shijie, LIU Qihe   

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu  610000, China
  • Online:2021-11-01 Published:2021-11-04



  1. 电子科技大学 信息与软件工程学院,成都 610000


Since the emergence of Deep Neural Network(DNN), the development of target tracking technology has also made great progress. Most of the current research in the field of target tracking is focused on improving the accuracy and efficiency of the algorithm in a computing environment with sufficient computing power. There are relatively few target tracking algorithms in a resource-constrained environment. Therefore, it is essential to develop a tracking network that is still effective in resource-constrained environments. This article systematically sorts out the progress and design concepts of target tracking technology in resource-constrained environments in recent years. This paper introduces the overall workflow of the target tracking task, and makes a summary based on the focus of each tracking method. This paper also summarizes the existing data set in the target tracking task and the evaluation indicators that can be used for model evaluation to facilitate research. The personnel determine the specific data set to be used according to the needs of the actual task. Existing work is combined to explore future research directions.

Key words: object tracking, deep learning, restricted resource



关键词: 目标跟踪, 深度学习, 资源受限