DETECTING AND TRACKING: ASSESSMENT OF WELL-ORGANIZED POSES IN VIDEOS DYNAMICALLY
Detection and tracking of human body key points in a multi-person video is the focus of this research article. In this, we use the most recent developments in video-based human-key point identification. Our technique uses key point estimate in frames or short video clips that include numerous people. Human position estimate and tracking is a newer method for locating a person's most important physical features. Human body language may be deciphered by computers using posture detection and tracking. It also aids in estimating the positions of human body parts and joints in photos and films, which is a huge benefit. It is Move Net, which uses temporal information from short video clips to anticipate quick and reliable outcomes that are utilized to estimate posture at the frame level. There are 17 critical points in an individual's movement that the Move Net model of motion estimation algorithm can identify. It is a highly quick and accurate model. Individual key points, and sometimes the affinities between them, are identified in a bottom-up model known as Move Net and then the predictions are aggregated into instances, which also employs heat maps to precisely locate key points on a human body. A feature extractor and a group of prediction heads make up this system's design. Multi-person video pose estimation, or MPII, is a technique we use to test different aspects of our model.
occlusions, key points, pose estimation, Move Net