基于人体关键点和YOLOv4的吸烟行为检测

Smoking behavior detection based on human keypoints and YOLOv4

基于机器视觉的吸烟行为检测主要利用深度学习对烟头进行目标检测,识别准确率不高。针对此问题,提出了一种基于人体关键点和YOLOv4的吸烟行为检测方法。在利用深度学习对烟头进行目标检测的基础上,加入人体关键点检测,通过计算距离、角度、时间周期判断吸烟动作是否发生。该方法首先利用AlphaPose、RetinaFace获得人体关键点位置信息;然后基于关键点位置信息计算手部与嘴部的距离、手、肘、肩膀之间的角度、吸烟的时间周期,设置吸烟行为规则;最后结合YOLOv4检测图像中是否有烟头,判断是否存在吸烟行为。实验结果表明,在自采集的吸烟数据中,该方法能够及时有效地检测出吸烟行为。

Smoking behavior detection in public places has become the focus of social attention.At present,smoking behavior detection based on machine vision mainly uses deep learning to detect cigarette ends and decide whether there is a smoking behavior by whether there are cigarette ends.Because the cigarette end target is small,the accuracy of this method is relatively low.Through a large number of observations,it is found that smoking behavior is rhythmic and cyclical.Therefore,a smoking behavior detection method based on human keypoints and YOLOv4 is proposed.On the basis of deep learning to detect cigarette butts,the keypoints detection of human body is added to judge whether smoking action occurs by calculating distance,angle and time cycle.Firstly,AlphaPose and RetinaFace are used to obtain the position information of key points of human body.Secondly,based on the position information of keypoints,the distance between hand and mouth,the angle between hand,elbow and shoulder,and the time period of smoking are calculated,and the smoking behavior rules are set.Finally,the cigarette end in the image is detected with YOLOv4 to determine whether there is smoking behavior.The experimental results show that the smoking behavior is detected timely and effectively in the self collected smoking data.