KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Object tracking based on adaptive updating of a spatial-temporal context model

  • Feng, Wanli (School of Computer and Information Technology, Beijing Jiaotong University) ;
  • Cen, Yigang (School of Computer and Information Technology, Beijing Jiaotong University) ;
  • Zeng, Xianyou (School of Computer and Information Technology, Beijing Jiaotong University) ;
  • Li, Zhetao (The college of information Engineering, Xiangtan University) ;
  • Zeng, Ming (School of Automation Science and Engineering, South China University of Technology) ;
  • Voronin, Viacheslav (Department of Radio-electronic systems, Don State Technical University)
  • Received : 2017.03.17
  • Accepted : 2017.07.24
  • Published : 2017.11.30
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Abstract

Recently, a tracking algorithm called the spatial-temporal context model has been proposed to locate a target by using the contextual information around the target. This model has achieved excellent results when the target undergoes slight occlusion and appearance changes. However, the target location in the current frame is based on the location in the previous frame, which will lead to failure in the presence of fast motion because of the lack of a prediction mechanism. In addition, the spatial context model is updated frame by frame, which will undoubtedly result in drift once the target is occluded continuously. This paper proposes two improvements to solve the above two problems: First, four possible positions of the target in the current frame are predicted based on the displacement between the previous two frames, and then, we calculate four confidence maps at these four positions; the target position is located at the position that corresponds to the maximum value. Second, we propose a target reliability criterion and design an adaptive threshold to regulate the updating speed of the model. Specifically, we stop updating the model when the reliability is lower than the threshold. Experimental results show that the proposed algorithm achieves better tracking results than traditional STC and other algorithms.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation, Natural Science Foundation of Guangdong Province

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