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Mixed pattern matching-based traffic abnormal behavior recognition.

Wu J, Cui Z, Sheng VS, Shi Y, Zhao P - ScientificWorldJournal (2013)

Bottom Line: 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.The real-world application verified its feasibility and the validity.

View Article: PubMed Central - PubMed

Affiliation: The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.

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.

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Related in: MedlinePlus

Pattern learning results of vehicle trajectories of an urban road. (a) The trajectory spatial patterns. (b) The trajectory direction patterns.
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Related In: Results  -  Collection


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fig11: Pattern learning results of vehicle trajectories of an urban road. (a) The trajectory spatial patterns. (b) The trajectory direction patterns.

Mentions: The clustering results of vehicle trajectories patterns in Figure 11 are obtained by the above clustering steps. We noticed that the clustering results are not four clusters. Main motion patterns are just three clusters. This indicates that vehicle traffic behavior patterns are affected by a variety of factors, rather than merely road settings. Again, we use different colors to mark different normal motion patterns. The experiments show that the obtained trajectory patterns using the clustering method proposed in this paper reflected the actual traffic situation.


Mixed pattern matching-based traffic abnormal behavior recognition.

Wu J, Cui Z, Sheng VS, Shi Y, Zhao P - ScientificWorldJournal (2013)

Pattern learning results of vehicle trajectories of an urban road. (a) The trajectory spatial patterns. (b) The trajectory direction patterns.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3926328&req=5

fig11: Pattern learning results of vehicle trajectories of an urban road. (a) The trajectory spatial patterns. (b) The trajectory direction patterns.
Mentions: The clustering results of vehicle trajectories patterns in Figure 11 are obtained by the above clustering steps. We noticed that the clustering results are not four clusters. Main motion patterns are just three clusters. This indicates that vehicle traffic behavior patterns are affected by a variety of factors, rather than merely road settings. Again, we use different colors to mark different normal motion patterns. The experiments show that the obtained trajectory patterns using the clustering method proposed in this paper reflected the actual traffic situation.

Bottom Line: 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.The real-world application verified its feasibility and the validity.

View Article: PubMed Central - PubMed

Affiliation: The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, China.

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.

Show MeSH
Related in: MedlinePlus