Behavior pattern extraction by trajectory analysis

doi:10.1007/s11704-010-0074-7

Front Comput Sci Chin

2011, Vol. 5

Issue (1) : 37-44 https://doi.org/10.1007/s11704-010-0074-7

RESEARCH ARTICLE

Behavior pattern extraction by trajectory analysis

Jia WEN^1,2(

), Chao LI¹, Zhang XIONG¹

1. School of Computer Science and Engineering, Beihang University, Beijing 100191, China; 2. Department of Computer, College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

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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.

Keywords trajectory clustering directional trimmed mean distance (DTMD) behavior pattern extraction

Corresponding Author(s): WEN Jia,Email:wjyanyuan@yahoo.com.cn

Issue Date: 05 March 2011

Cite this article:

Jia WEN,Chao LI,Zhang XIONG. Behavior pattern extraction by trajectory analysis[J]. Front Comput Sci Chin, 2011, 5(1): 37-44.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-010-0074-7
https://academic.hep.com.cn/fcs/EN/Y2011/V5/I1/37

Fig.1 Sketch map of trajectory scene

Fig.2 Original scene in real world

Fig.3 Clustering process graph

Fig.4 A dendrogram of hierarchical clustering

Fig.5 Result of hierarchical clustering

Tab.1 Comparison between Wang [], Buzan [], Atev [] and our method

Fig.6 Result of behavior pattern extraction

Fig.7 Result of trajectories from each cluster

Fig.8 Examples of Abnormal Detection

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