摘要
针对监控视频识别突发事件效率低,导致识别系统无法及时检测并响应突发事件,增加潜在危险的问题,提出应用计算机视觉的监控图像异常行为识别算法。以监控图像的初始背景为基础,利用差分运算获取背景与监控图像差分后的差分图像,并利用背景减除法对组合排序后的新监控图像实施二值化处理,完成目标区域识别;然后利用矩形遍历目标区域,采集目标区域的有效运动块,提取运动块的特征向量,完成监控图像异常行为特征提取;最后通过库恩塔克条件,完成监控图像异常行为识别。实验结果表明,所提方法的异常行为识别时间在1.0 s以内,识别准确率保持在94%以上,可准确识别监控图像异常行为,有效提高识别效率与识别率。
The low efficiency of video surveillance in identifying emergencies results in that the recognition system is unable to detect and respond to emergencies in a timely manner,increasing the risk of potential hazards.Therefore,a recognition algorithms of monitoring image abnormal behavior based on computer vision is proposed.Based on the initial background of the monitoring image,a differential operation is used to obtain the differential image between the background image and the monitoring image,and the background subtraction method is used to perform binary processing on the combined sorted new monitoring image to complete target area recognition.Then,a rectangle is used to traverse the target area,collect effective motion blocks from the target area,extract the feature vectors of the motion blocks,and complete the extraction of abnormal behavior features in the monitoring image.And the identification of abnormal behavior in monitoring images through Kuhntak conditions is completed.The experimental results show that the proposed method has an abnormal behavior recognition time of less than 1.0 s,and the recognition accuracy remains above 94%.It can accurately identify abnormal behavior in monitoring images,effectively improving recognition efficiency and recognition rate.
作者
郭祥葛
GUO Xiangge(School of Engineering,Ocean University of China,Qingdao 266100,China;Yantian Power Supply Bureau Distribution Network Asset Department,Shenzhen Power Supply Company Limited,Shenzhen 510700,China)
出处
《吉林大学学报(信息科学版)》
2025年第3期682-688,共7页
Journal of Jilin University(Information Science Edition)
基金
基于AI应用的配电房智能运维关键技术研究基金资助项目(090000KK52222051)。
关键词
计算机视觉
背景模型
特征提取
径向基核函数
LSSVM模型
computer vision
background model
feature extraction
radial basis kernel function
least squares support vector machine(LSSVM)model
