Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition
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 trajec...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/834013 |
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| _version_ | 1832545472274759680 |
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| author | Jian Wu Zhiming Cui Victor S. Sheng Yujie Shi Pengpeng Zhao |
| author_facet | Jian Wu Zhiming Cui Victor S. Sheng Yujie Shi Pengpeng Zhao |
| author_sort | Jian Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d01695927aae40129f1c22b74c295741 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d01695927aae40129f1c22b74c2957412025-02-03T07:25:48ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/834013834013Mixed Pattern Matching-Based Traffic Abnormal Behavior RecognitionJian Wu0Zhiming Cui1Victor S. Sheng2Yujie Shi3Pengpeng Zhao4The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaDepartment of Computer Science, University of Central Arkansas, Conway, AR 72035, USAThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaA 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.http://dx.doi.org/10.1155/2014/834013 |
| spellingShingle | Jian Wu Zhiming Cui Victor S. Sheng Yujie Shi Pengpeng Zhao Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition The Scientific World Journal |
| title | Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition |
| title_full | Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition |
| title_fullStr | Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition |
| title_full_unstemmed | Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition |
| title_short | Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition |
| title_sort | mixed pattern matching based traffic abnormal behavior recognition |
| url | http://dx.doi.org/10.1155/2014/834013 |
| work_keys_str_mv | AT jianwu mixedpatternmatchingbasedtrafficabnormalbehaviorrecognition AT zhimingcui mixedpatternmatchingbasedtrafficabnormalbehaviorrecognition AT victorssheng mixedpatternmatchingbasedtrafficabnormalbehaviorrecognition AT yujieshi mixedpatternmatchingbasedtrafficabnormalbehaviorrecognition AT pengpengzhao mixedpatternmatchingbasedtrafficabnormalbehaviorrecognition |