监控视频中的车辆异常行为检测

Detection of Vehicle's Abnormal Behaviors in Surveillance Video

针对人工监测道路上车辆超速、违规变道和闯红灯等车辆异常行为各种弊端,提出了一种基于监控视频的车辆异常行为检测方法.首先使用ViBe(Visual Background Extractor)算法得到车辆的前景图像,利用金字塔Lucas-Kanada光流法跟踪前景图像中的强角点并计算出该点的速度和角度,再利用均值漂移算法对速度和角度两个运动特征标量聚类,经统计得到聚类后的统计直方图.最后,分别通过运动特征熵和运动特征标量到聚类中心的欧式距离2种方法判断车辆有无异常行为.实验结果表明,2种方法能够准确、实时地检测出道路中的车辆异常行为.

In view of the vehicle＇s abnormal behaviors in the artificial monitoring, such as speeding, illegal lane changing and red light running, this study proposes a method for detecting abnormal behaviors of vehicles based on video analysis technology. First, it uses ViBe（Visual Background Extractor） method to get the foreground image. It tracks the corners by using the Lucas-Kanada optical flow method, getting the corners velocity and direction information. Then, it uses the mean shift method to cluster the two motion features to get the statistical histogram after clustering. Finally, it judges the abnormal behavior of the vehicle with the Euclidean distance of the motion characteristic entropy and the two motion characteristic scalars to the cluster center. The experimental result shows that the two methods can detect vehicle＇s abnormal behaviors accurately and in real time.