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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

The DTW algorithm for minimum distortion.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: The DTW algorithm for minimum distortion.

Mentions: The principle of the DTW algorithm in the trajectory similarity measurement is as follows. Assume that trajectory T has N tracepoints, and trajectory R has M tracepoints. The number of tracepoints of trajectory T is marked as the horizontal coordinate, and the number of tracepoints of trajectory R is marked as the vertical coordinate, as shown in the principle diagram Figure 1. So the relationships among tracepoint numbers form a grid. Any cross point (N, M) in the grid indicates the intersection of trajectory T(xn, yn) and trajectory R(xm, ym), and the intersection point has a distance metric D(T(xn, yn), R(xm, ym)).


Mixed pattern matching-based traffic abnormal behavior recognition.

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

The DTW algorithm for minimum distortion.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: The DTW algorithm for minimum distortion.
Mentions: The principle of the DTW algorithm in the trajectory similarity measurement is as follows. Assume that trajectory T has N tracepoints, and trajectory R has M tracepoints. The number of tracepoints of trajectory T is marked as the horizontal coordinate, and the number of tracepoints of trajectory R is marked as the vertical coordinate, as shown in the principle diagram Figure 1. So the relationships among tracepoint numbers form a grid. Any cross point (N, M) in the grid indicates the intersection of trajectory T(xn, yn) and trajectory R(xm, ym), and the intersection point has a distance metric D(T(xn, yn), R(xm, ym)).

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