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Behavior pattern extraction by trajectory analysis

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Abstract

Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance (DTMD), a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory’s classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Jia Wen, Chao Li & Zhang Xiong

  2. Department of Computer, College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Jia Wen

Authors
  1. Jia Wen
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  2. Chao Li
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  3. Zhang Xiong
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Correspondence to Jia Wen.

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Wen, J., Li, C. & Xiong, Z. Behavior pattern extraction by trajectory analysis. Front. Comput. Sci. China 5, 37–44 (2011). https://doi.org/10.1007/s11704-010-0074-7

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  • DOI: https://doi.org/10.1007/s11704-010-0074-7

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