基于车牌识别系统车辆轨迹的行为异常检测  

Activity Anomaly Detection Based on Vehicle Trajectory of Automatic Number Plate Recognition System

作　　者：孙玉砚[1,2] 孙利民[1] 朱红松[1] 周新运[1] 

机构地区：[1]物联网信息安全技术北京市重点实验室(中国科学院信息工程研究所),北京100093 [2]中国科学院大学,北京100049

出　　处：《计算机研究与发展》2015年第8期1921-1929,共9页Journal of Computer Research and Development

基　　金：国家"八六三"高技术研究发展计划基金项目(2012AA050804);国家科技支撑计划基金项目(2012BAH20B03);中国科学院战略性先导科技专项(XDA06040100)

摘　　要：目前已有很多面向智能交通管理的车辆异常行为检测方法,但是在公共安全领域的异常行为检测研究不足.为此提出了一种基于车牌识别系统车辆轨迹的行为异常检测机制,通过车牌识别系统获取抓拍记录,分析各个车辆在系统卡口的历史通行记录,提取车辆轨迹的时间空间特征,通过空间特征发现异常路线并计算路线的围绕质心累积转动角度值检测徘徊行为,用聚类算法获得时间特征的聚类中心并计算离群点检测特殊时间活跃行为.利用实际部署的车牌识别系统收集的数据测试了所提出的异常检测方法,实验结果表明该检测方法能够很好地检测面向公共安全领域的车辆异常行为,在卡口设备识别率不理想的情况下有效地提高了异常检测率.Anomaly detection acts as the major direction of intelligent traffic management, but current studies may not yield the best results in the field of public safety. This paper proposes a machine- learning based technique to detect vehicle anomalies from vehicle trajectory data captured by automatic number plate recognition （ANPR） system. Our scheme is capable of detecting vehicles with the behavior of wandering round and unusual activity at specific time. Firstly the spatial and temporal quantitative indicators of vehicle activity features are extracted from historical vehicle trajectory data. The vehicles with unusual spatial feature are found and their cumulative rotation angles around the centroid of the route are calculated to detect Spatial wandering round behavior. The distance from the center of clusters created by K-means classification algorithm based on the temporal features vectors are computed to find outliers. We collecte the records from ANPR system with 315 cameras deployed in real-world for more than two months, and over 5.4 million vehicles are captured. The evaluation results based on the data set show the efficiency of the anomaly detection. More importantly, our scheme can significantly improve the detection robustness especially when the data collected by the ANRP system are noisy due to poor weather condition.

关 键 词：异常检测 数据挖掘 特征向量 聚类算法 轨迹分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]