摘要
论文提出一种基于改进的时空卷积神经网络加上YOLOv5识别人员是否携带武器的方法,此方法首先从边境的监控录像中提取单帧为单元提取人体骨架点信息,然后以时空图卷积神经网络为框架聚合多帧图像信息,判别人员动作。再通过YOLOv5和人体骨架检测人员和武器之间的关系,判断运动的人员是否携带武器。最后通过实验验证方法的有效性,结果表明该方法可以充分利用多帧图像中的骨架点之间的时空信息,来准确识别人员运动情况,以及是否携带武器,具有很好的准确率和鲁棒性。
This paper proposes a method based on the improved ST-GCN and YOLOv5 to identify whether a person is carrying a weapon.In this method,a single frame is extracted from the border surveillance video as a unit to extract the skeleton point infor-mation of the human body,and then multi-frame image information is aggregated by using the ST-GCN as a framework to identify the movement of the person.Then,the relationship between personnel and weapons can be detected by YOLOv5 and human skele-ton to determine whether personnel in motion carry weapons.Finally,experiments are conducted to verify the effectiveness of the method.The results show that the method can make full use of the spatio-temporal information between the skeleton points in multi-frame images to accurately identify the movement of people and whether they carry weapons,with good accuracy and robustness。
作者
黄兆年
卢龙生
程朋
李恒
HUANG Zhaonian;LU Longsheng;CHENG Peng;LI Heng(Wuhan Digital Engineering Institute,Wuhan 430205;Guangzhou Changdao Optoelectronic Machinery Factory,Guangzhou 510336)
出处
《舰船电子工程》
2025年第3期38-42,共5页
Ship Electronic Engineering
