摄像头监控区域偏差检测是高速公路异常事件自动检测及其他监控视频内容分析的前提和基础． 由于高速公路监控场景中存在运动对象、光线、噪声等干扰，现有偏差检测算法存在实时性差、抗干扰性不足等问题，尚难以满足应用需求． 为此，文中提出了一种基于角点集特征的偏差检测方法． 首先基于角点处的泰勒级数极值大、随机噪点的可聚类性低等特点剔除伪角点，并利用训练得到的角点集特征准确地表征图像． 在此基础上，采取具有抗光线扰动的角点集信息进行匹配，并通过互相关和动态阈值方法分别实现角点位置和数量的匹配，以减少光线等的干扰，实现偏差事件的准确检测． 对比实验结果表明，所提方法在提高算法实时性的同时，能够保证 92% 的平均检测率，较好地满足了高速公路监控的应用需求．

For the automatic detection of abnormal accidents on highways and the analysis of other video contents,the monitoring range offset detection is the precondition and basis.Due to the interference of moving objects,light and noises in the highway scenes,the existing detection methods show poor real-time performance and robustness.In order to solve these problems,an offset detection method based on corner set features is proposed.In this meth- od,first,false corners are removed according to the characteristics of the Taylor series,such as great extremism and few clusters of random noisy points.Then,the trained corner set features are used to accurately represent the image.On this basis,a matching is conducted by employing the corner set information rejecting the light interfer- ence,and both the cross correlation method and the dynamic threshold are adopted to realize the matching of the corner position and the number respectively,so as to avoid such interferences as light.Thus,the accurate detec- tion of deviation events is accomplished.The results of comparative experiments show that the proposed method im- proves the real-time performance and still maintains an average detection rate of 92%,which can better meet the requirement of highway monitoring.