校园围栏场景下的异常行为检测系统  

Abnormal behavior detection system in campus fence scenes

作　　者：李瑷嘉 彭新茗 马广焜[1] 陈展鹏 于洋 LI Aijia;PENG Xinming;MA Guangkun;CHEN Zhanpeng;YU Yang(School of Software,Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]沈阳工业大学软件学院,沈阳110870

出　　处：《智能计算机与应用》2023年第4期174-177,共4页Intelligent Computer and Applications

基　　金：辽宁省2021年大学生创新创业训练计划项目(S202110142041)。

摘　　要：针对校园封闭管理下的学生翻越围栏、偷取外卖等现象,提出一种基于YOLOv5的校园围栏场景下行人异常行为检测系统。该系统首先对监控视频中提取的图像进行网络训练,模型训练完成后以此来检测视频中的翻越、攀爬栅栏围墙等异常行为。当检测到与围栏距离过近的人员存在疑似异常行为时,系统触发警报模块,警示学生保持与围栏间的距离。In view of the phenomenon of students jumping over the fence and stealing takeout in the closed campus during the prevention and control period,a YOLOv5 based pedestrian abnormal behavior detection system under the campus fence scenario is proposed.Firstly,the system carries out network training on the images extracted from the surveillance video,and uses the trained offline network model to automatically monitor the abnormal behaviors of people in the surveillance video,such as jumping over and climbing.When detecting the suspected abnormal behavior of those closed to the fence,the system could issue an alarm to remind students to keep the distance from the fence.

关 键 词：深度学习 异常行为检测 YOLOv5 

分 类 号：TN911.73[电子电信—通信与信息系统]