LAUSR

Human detection and motion recovery are the hotspots in the field of artificial intelligence and computer vision. They are widely used in vehicle-assisted systems, intelligent monitoring systems, human-computer interaction, and sports training. The human body detector based on the overall feature is to calculate the feature distribution in the entire rectangular detection window, and requires most of the information of the entire human body, so the performance of this method will be significantly reduced when encountering partial occlusion or complex posture. Firstly, this paper proposes multiple parts to express the whole human body, and divides the target into several separate parts such as head, trunk and limbs, and divides and treats the individual training part classifier for the human body target of complex human posture and partial occlusion. Secondly, this paper gives a kind of a method of restoring a three-dimensional motion posture of a human body from a monocular video sequence. The experimental results show that the method can be applied to human detection with multi-pose and partial occlusion in complex background, which provides an effective technical means for human detection in complex background. At the same time, the method does not need to mark, and the recovered relative motion posture of the human body is simple and effective, the optimization result is robust, and the recovery result can satisfy the human motion posture analysis with low precision requirement.