Research On 3D Human Motion Generation In Dynamic Occlusion Scene

In recent years,with the development of deep learning technology,threedimensional human motion generation methods have attracted much attention in the field of computer vision.It has important practical significance and broad application value in fields such as virtual reality and motion video analysis.Although many image and video based 3D human pose and shape estimation methods have been proposed,these methods mainly focus on the generation of 3D human motion for a complete human body in a single scene.However,real scenes are complex.This article focuses on the generation of 3D human motion for dynamic occlusion scenes,To improve the generation accuracy of 3D human motion sequences when there is no occlusion between the human bodies and when occlusion occurs.Finally,based on the 3D human body generation algorithm in this article,a 3D human motion generation system was designed and implemented.The main innovations of the paper are as follows:(1)To address the problem of low accuracy of pedestrian occlusion detection in dynamic scenes,an inter-human occlusion detection method based on the Yolov5(You Only Look Once)network combined with an improved background difference method is proposed to reduce the influence of image background noise.First,considering that the background difference method is susceptible to lighting factors,the luminance components of the acquired RGB image frames are extracted,and the luminance components of the current frame and the background frame are normalized;then the background difference operation is performed,and the human target detection network is used for human body recognition;finally,calculate the coordinates of the human detection frame to obtain the centroid coordinates of the human body,and determine whether there is obstruction between the human bodies based on the distance between the centroids.In addition,in order to verify the effectiveness of the proposed human occlusion detection method,a comparison experiment is conducted,and the experimental results show that the proposed human occlusion detection method has a better detection effect on the detection of human occlusion in dynamic scenes and improves the detection accuracy.(2)To address the problem of poor 3D human motion sequence generation in occlusion scenes,a 3D human motion generation method based on the improved PHD(Predicting 3D Human Dynamics from Video)algorithm is proposed.First,in order to improve the accuracy of human key point detection in PHD,channel splitting and channel rearrangement operations are introduced in the human key point detection network to enhance its nonlinear capability;then,a 3D human prediction method is used to predict and generate human motion sequences in obscured scenes based on the human motion sequences and their key point locations in unobscured scenes.In addition,in order to verify the effectiveness of the proposed 3D human motion sequence generation method in the occluded scene,relevant comparison experiments are conducted on the mainstream dataset Human3.6M.The experimental results show that the proposed 3D human motion generation method with the improved PHD algorithm has a better generation effect on the human motion in the occluded scene and improves the 3D human motion generation accuracy.