title: Mixed pattern matching-based traffic abnormal behavior recognition.
abstract: 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.
authors.affiliation: The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.
authors.forename: Jian
authors.identifier: 0000-0002-8759-7107
authors.initials: J
authors.lastname: Wu
authors.affiliation: The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.
authors.forename: Zhiming
authors.initials: Z
authors.lastname: Cui
authors.affiliation: Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA.
authors.forename: Victor S
authors.identifier: 0000-0003-4960-174X
authors.initials: VS
authors.lastname: Sheng
authors.affiliation: The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.
authors.forename: Yujie
authors.identifier: 0000-0002-3748-0680
authors.initials: Y
authors.lastname: Shi
authors.affiliation: The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.
authors.forename: Pengpeng
authors.initials: P
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D009038:Motion; D018986:Motor Vehicles; D010363:Pattern Recognition, Automated