# Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences
## 1 Introduction
### Motion Capture  
The current motion capture devices are mainly divided into the following five categories based on different technical principles: (1) Optical markerless motion capture devices, which usually use depth cameras or calibrated normal cameras to obtain 3D skeleton point coordinates of the human body based on RGB color images or depth images. This method has the advantages of easy deployment (e.g., iPi Soft), and being low priced. It has been widely used in motion capture of video games, and some scholars have used this platform as a data acquisition device for scientific research [1; 13; 14; 15]. Modern functional evaluation and biomechanical research are mostly based on the use of optoelectronic systems. [PERSON] et al. reviewed the working principle of this system, its application, and settings in the clinical practice [16]. [PERSON] et al. used optoelectronic stereophotogrammetric data to address the issues related to the 3D reconstruction of motion and analysis of skeletal system kinematics in vivo [17]; (2) Optical marker motion capture devices, which achieve motion capture with active or passive markers placed on key points of the body surface. This method is highly accurate but expensive (e.g., Vicon). [PERSON] et al. utilized the \"Vicon 8\" motion capture system to capture the movements of a dancer in 3D and modified the system to control digital music [18]; (3) Mechanical motion capture devices, which perform attitude perception through mechanical wearable devices. This method is cheap, but the people move unnaturally because of the influence of mechanical equipment (e.g., Animazoo Gypsy). There is a wearable technology market for athletes and coaches, feedback from the device allows them to focus more on the movements they are performing and enables them to perform biomechanical changes without a coach [19]; (4) Magnetic motion capture devices, which calculate the position and orientation by the relative magnetic flux of the three orthogonal coils of the transmitter and receiver. This method is less expensive, but the return value at the boundary is not accurate due to nonlinear characteristics (e.g., Ascension MotionStar). [PERSON] et al. reported a typical root mean square error (RMSE) of approximately 7\({}^{\circ}\) for the magnetic, angular rate and gravity (MARG) sensors for the detection of upper limb motion in cricket [20]; (5) Inertial motion capture devices, which collect the attitude orientation of body parts through the wireless action attitude sensor, and recover the human motion model based on the principle of human kinematics. This method is moderately accurate, suitable for outdoor activities with good portability, but the external wear sensor is easy to move, resulting in measurement errors (e.g., Xsens). Low-power and low-cost electronic sensors have been successfully implemented through direct measurement by accelerometers, which have been used to continuously monitor patients in clinical and home environments [21]. [PERSON] et al. investigated the use of a low-cost instrumenting gait with an accelerometer, and they observed that appropriate gait algorithms were an effective tool for estimating total steps and average spatiotemporal gait characteristics [22]. [PERSON] et al. discussed the computational algorithms for classifying human physical activity using on-body accelerometers [23]. [PERSON] et al. identified, selected, and categorized the methodologies for estimating the ground reaction forces from kinematic data obtained by inertial measurement units worn by the subject, laying the foundation for kinetic analysis and motion performance testing outside the laboratory [24].