3-D human pose measurement method based on deep learning and epipolar constraint

It is necessary for the design of car seat to realize the high precision and real-time measurement of the 3-D human pose on the car seat. Based on the existing 2-D and 3-D human joint measurement methods, a 3-D human pose measurement method is proposed, which integrates deep learning and epipolar constraint. This method combines 2-D human joint depth network extraction method with binocular measurement system, uses two-channel multi-stage iterative network to extract 2-D human joint points in the images taken by left and right cameras respectively, combines the Brief feature of joint point position and epipolar constraint, uses binocular camera calibration results to convert the matching 2-D joint point information into 3-D space, and finally obtains 3-D human pose. The experimental results show that the detection accuracy of the proposed method in the self sampling test set can reach 98%. The deviation of the angle of the key position of human body calculated by the 3-D joint point is less than 10°. Therefore, the method proposed in this paper can meet the data acquisition requirements of the actual car seat design.