Mixed pattern matching-based traffic abnormal behavior recognition.
Mixed pattern matching-based traffic abnormal behavior recognition.
复制标题
基于混合模式匹配的交通异常行为识别
DOI：
10.1155/2014/834013
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发表时间：
2014
影响因子：
--
通讯作者：
Zhao P
中科院分区：
文献类型：
--
作者：
Wu J;Cui Z;Sheng VS;Shi Y;Zhao P
运动轨迹是运动目标微动行为在时空域上的直观表现形式。轨迹分析是识别运动目标异常行为的重要方法。针对车辆轨迹的复杂性，提出了一种基于动态时间规整（DTW）和谱聚类的轨迹模式学习方法。该算法引入DTW距离来度量车辆轨迹之间的距离，并通过基于距离矩阵的谱聚类算法自动确定聚类数目。然后，将样本数据点聚类到不同的聚类中。在对聚类结果进行空间模式和方向模式学习的基础上，提出了一种基于混合模式匹配的车辆异常行为识别方法。实验结果表明，该技术方案能有效识别主要类型的流量异常行为，具有较好的鲁棒性。实际应用验证了其可行性和有效性。
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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影响因子：
4.7
作者：
Micheloni, Christian;Snidaro, Lauro;Foresti, Gian Luca
通讯作者：
Foresti, Gian Luca
相关相似性测度空间中的文档聚类
影响因子：
8.9
作者：
Zhang, Taiping;Tang, Yuan Yan;Xiang, Yong
通讯作者：
Xiang, Yong
影响因子：
19.5
作者：
Wang, Heng;Klaeser, Alexander;Liu, Cheng-Lin
通讯作者：
Liu, Cheng-Lin
影响因子：
5.8
作者：
Ding, CHQ
通讯作者：
Ding, CHQ
DOI：
10.1109/tsmcb.2012.2185694
发表时间：
2012-08-01
影响因子：
--
作者：
Vakanski, Aleksandar;Mantegh, Iraj;Janabi-Sharifi, Farrokh
通讯作者：
Janabi-Sharifi, Farrokh