OpenPose is a library that t using a two-branch convolutional neural network allows for the recognition of skeletons in the scene.  Although OpenPose-based solutions are spreading, their metrological performances relative to video setup are still largely unexplored.  This paper aimed at validating a two-cameras OpenPose-based markerless system for gait analysis, considering its accuracy relative to three factors: cameras' relative distance, gait direction and video resolution.  Procedures involved: calibration of the stereoscopic system; acquisition of video recordings, simultaneously with the reference marker-based system; video processing within OpenPose to extract the subject's skeleton; videos synchronization; triangulation of the skeletons in the two videos to obtain the 3D coordinates of the joints.  The OpenPose-based system tended to underestimate step length of about 1.5 cm, while no systematic biases were found for swing/stance time.  These findings confirm the feasibility of tracking kinematics and gait parameters of a single subject in a 3D space using two low-cost webcams and the OpenPose engine. 
OpenPose
This paper aimed at validating a two-cameras OpenPose-based markerless system for gait analysis, considering its accuracy relative to three factors: cameras' relative distance, gait direction and video resolution. 
OpenPose-based markerless system