计算机应用研究2024，Vol.41Issue(1)：306-313,320,9.DOI:10.19734/j.issn.1001-3695.2023.04.0225
面向边缘端设备的轻量化视频异常事件检测方法
Lightweight video abnormal event detection method for edge devices
摘要
Abstract
Existing CNN-based video anomaly detection methods improve the accuracy continuously,which are faced with is-sues such as complex architecture,large parameters and lengthy training.Therefore,the hardware computing power require-ments of them are high,which makes it difficult to adapt to edge devices with limited computing resources like UAVs.To this end,this paper proposed a lightweight abnormal event detection method for edge devices.Firstly,the method extracted gradient cuboids and optical flow cuboids from video sequence as appearance and motion feature representation.Secondly,the method designed a modified PCANet network to obtain high-level block-wise histogram features of gradient cuboids.Then,the method calculated the appearance anomaly score of each block based on histogram feature distribution,and calculated the motion ano-maly score based on the accumulation of optical flow amplitudes of internal pixels.Finally,the method fused the appearance and motion anomaly scores to identify anomalous blocks,achieving appearance and motion abnormal events detection and localization simultaneously.The frame-level AUC of proposed method reached 86.7％on UCSD Ped1 dataset and 94.9％on UCSD Ped2 dataset,which were superior to other methods and the parameters were much smaller.Experimental results show that the method achieves better anomaly detection performance under low computational power requirements,making the ba-lance between detection precision and computing resources,which is suitable for low-power edge devices.关键词
智能视频监控/边缘端设备/异常事件检测/主成分分析网络/分块直方图特征Key words
intelligent video surveillance/edge device/abnormal event detection/principle component analysis network/block-wise histogram feature分类
信息技术与安全科学引用本文复制引用
李南君,李爽,李拓,邹晓峰,王长红..面向边缘端设备的轻量化视频异常事件检测方法[J].计算机应用研究,2024,41(1):306-313,320,9.基金项目
山东省自然科学基金资助项目(ZR2023QF050) （ZR2023QF050）
国家自然科学基金资助项目(62203242) （62203242）
