Advanced Sports Performance Analysis using Deep Learning for Posture and Movement Identification

The proposed work explains the method of detecting the area of interest (ROI), extracting pertinent information, distinguishing different postures, and categorising objects, activities, or events by employing recovered characteristics and segmented regions. Furthermore, the paper delves into the challenges of analysing human posture in three dimensions and advises using 3DPoseNet to estimate the 3D coordinates of vertices. Furthermore, the essay investigates the occlusion problem and proposes a general solution. This style involves extensive use of the torso as well as the limbs and head. Finally, the paper emphasises the creation and implementation of deep learning (DL) in motion identification, which has increased detection performance while decreasing complexity. The article emphasises the importance of developing a variety of algorithms and strategies, such as object tracking, event detection, and action identification, as well as combining methods from image processing, machine learning, and computer vision in order to analyse and interpret video data in applications such as surveillance, sports analysis, and medical imaging. Surveillance, sports analysis, and medical imaging are examples of these uses.