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The Scientific World Journal
Volume 2014 (2014), Article ID 834013, 12 pages
Research Article

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

1The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China
2Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA

Received 18 August 2013; Accepted 14 November 2013; Published 27 January 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Jian Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity.