Optical Unmarked Motion Capture Technology Based on Depth Network and Binocular Vision
Ye Li, Wenjie Chen, Qing You, Yangyang Sun, Jing Li · 2019
This paper presents an optical unmarked motion capture method based on depth network and binocular vision. This method optimizes the marked motion capture technology, eliminating the need for additional markers to reduce the complexity of the motion capture system. At the same time, this paper also optimizes the human joint point coding method, which can obtain the sequence numbers and interdependence of 18 human joint points including the toes of the human body. Then we utilize the deep convolutional neural network to extract the coordinates of the two-view 2D human joint points. Through the binocular vision principle and the least squares method, the 3D coordinates of the human joint points are obtained. According to this, the human skeleton model is drawn to reflect the human body motion state.