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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

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 WEN1,2(), 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.1  Sketch map of trajectory scene
Fig.2  Original scene in real world
Fig.2  Original scene in real world
Fig.3  Clustering process graph
Fig.3  Clustering process graph
Fig.4  A dendrogram of hierarchical clustering
Fig.4  A dendrogram of hierarchical clustering
Fig.5  Result of hierarchical clustering
Fig.5  Result of hierarchical clustering
Clustering methodCorrect clustering numberCorrect rate/%
Cluster ⅠCluster ⅡCluster ⅢCluster ⅣclusterⅤ
Real scene by eyes8448705640100
Wang [1]834567503995.3
Buzan [12]803963543390.27
Atev [15]824764553996.31
Our method DTMD844866564098.66
Tab.1  Comparison between Wang [], Buzan [], Atev [] and our method
Fig.6  Result of behavior pattern extraction
Fig.6  Result of behavior pattern extraction
Fig.7  Result of trajectories from each cluster
Fig.7  Result of trajectories from each cluster
Fig.8  Examples of Abnormal Detection
Fig.8  Examples of Abnormal Detection
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