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.

smoking detection; human keypoints; YOLOv4; periodicity; target detection