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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 a highway road. (a) The spatial patterns of trajectories. (b) The direction patterns of trajectories.
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Related In: Results  -  Collection


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fig6: Pattern learning results of vehicle trajectories of a highway road. (a) The spatial patterns of trajectories. (b) The direction patterns of trajectories.

Mentions: We perform clustering based on DTW and spectral clustering on trajectory sequences obtained after redundancy removal, and the results are shown in Figure 6. Figure 6(a) shows the clustering result of the spatial patterns of trajectories. We can also see that the trajectory sequences are separated into three trajectory clusters via the DTW spatial distance and spectral clustering, which are distinguished with three different colors. The traffic scene of this experimental video has three running lanes. Under normal driving, there exist three spatial behavior patterns.


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 a highway road. (a) The spatial patterns of trajectories. (b) The direction patterns of trajectories.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Pattern learning results of vehicle trajectories of a highway road. (a) The spatial patterns of trajectories. (b) The direction patterns of trajectories.
Mentions: We perform clustering based on DTW and spectral clustering on trajectory sequences obtained after redundancy removal, and the results are shown in Figure 6. Figure 6(a) shows the clustering result of the spatial patterns of trajectories. We can also see that the trajectory sequences are separated into three trajectory clusters via the DTW spatial distance and spectral clustering, which are distinguished with three different colors. The traffic scene of this experimental video has three running lanes. Under normal driving, there exist three spatial behavior patterns.

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