Computer vision-based approach for skeleton-based action recognition, SAHC

Given their small size and low weight, skeleton sequences are a great option for joint-based action detection. Recent skeleton-based action recognition techniques use feature extraction from 3D joint coordinates as per spatial-temporal signals, fusing these exemplifications in a motion context to improve identification accuracy. High accuracy has been achieved with the use of first- and second-order characteristics, such as spatial, angular, and hough representations. In contrast to the and hough transform, which are useful for encoding summarized independent joint coordinates motion, the spatial, and angular features all higher-order representations are discussed in this article for encoding the static and velocity domains of 3D joints. When used to represent relative motion between body parts in the human body, the encoding is effective and remains constant across a wide range of individual body sizes. However, many models still become confused when presented with activities that have a similar trajectory. Suggest addressing these problems by integrating spatial, angular, and hough encoding as relevant order elements into contemporary systems to more accurately reflect the interdependencies between components. By combining these widely-used spatial-temporal characteristics into a single framework SAHC, acquired state-of-the-art performance on four different benchmark datasets with fewer parameters and less batch processing.