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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

Illegal retrograde behavior recognition. (a) An abnormal trajectory. (b) The matching results of the spatial pattern. (c) The matching results of the mixed pattern.
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fig8: Illegal retrograde behavior recognition. (a) An abnormal trajectory. (b) The matching results of the spatial pattern. (c) The matching results of the mixed pattern.

Mentions: We still use the above highway traffic scene. On highway, it sometimes appears risking behaviors like illegal retrograde. In Figure 8(a), the motion trajectory marked with pink is an abnormal illegal retrograde. This kind of abnormal behaviors can be detected using the above clustering approach. Figure 8(b) shows the spatial pattern-matching result of the test trajectory. From the figure, we can see that the vehicle traveled in the right motion pattern at beginning, that is, the motion pattern marked in green. The probability belonging to the green pattern is 1, and the probability belonging to other patterns (red and blue) is 0. With the gradual shift of the vehicle, it shifted to the blue motion pattern. Meanwhile, its motion direction shifted from forward to backward gradually, and finally it ran in the blue motion pattern in a reverse direction. This has been reflected by Figure 8(b). Figure 8(b) shows that the probability of the green motion pattern decreases gradually, downward to 0. The probability of the blue motion pattern increases gradually, upward to 1. The probability of the red motion pattern always keeps as 0. After integrating direction pattern matching with the basis of spatial pattern-matching result, the final abnormal recognition result is shown in Figure 8(c).


Mixed pattern matching-based traffic abnormal behavior recognition.

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

Illegal retrograde behavior recognition. (a) An abnormal trajectory. (b) The matching results of the spatial pattern. (c) The matching results of the mixed pattern.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: Illegal retrograde behavior recognition. (a) An abnormal trajectory. (b) The matching results of the spatial pattern. (c) The matching results of the mixed pattern.
Mentions: We still use the above highway traffic scene. On highway, it sometimes appears risking behaviors like illegal retrograde. In Figure 8(a), the motion trajectory marked with pink is an abnormal illegal retrograde. This kind of abnormal behaviors can be detected using the above clustering approach. Figure 8(b) shows the spatial pattern-matching result of the test trajectory. From the figure, we can see that the vehicle traveled in the right motion pattern at beginning, that is, the motion pattern marked in green. The probability belonging to the green pattern is 1, and the probability belonging to other patterns (red and blue) is 0. With the gradual shift of the vehicle, it shifted to the blue motion pattern. Meanwhile, its motion direction shifted from forward to backward gradually, and finally it ran in the blue motion pattern in a reverse direction. This has been reflected by Figure 8(b). Figure 8(b) shows that the probability of the green motion pattern decreases gradually, downward to 0. The probability of the blue motion pattern increases gradually, upward to 1. The probability of the red motion pattern always keeps as 0. After integrating direction pattern matching with the basis of spatial pattern-matching result, the final abnormal recognition result is shown in Figure 8(c).

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