Integrating Human Motion Dynamics in CNN Architecture to Recognize Human Activity from Different Camera Angles

Abstract

Human Activity Recognition (HAR) is a crucial component of computer vision, with applications in human-computer interaction and surveillance. As the need for HAR technology keeps increasing, so does the desire for solutions that can help people train by showcasing professional moves. For instance, new recruits can be successfully trained in particular fighting skills by observing the activities of seasoned soldiers. In order to increase the accuracy and dependability of HAR systems, this study investigates the incorporation of human motion dynamics into Convolutional Neural Network (CNN) architectures. This study enhances CNN’s ability to capture both spatial and temporal features by incorporating dynamic changes in human movement as additional inputs, which results in a more complex comprehension of human activity. A significant identification of complex human activity and frequent movement is made viable by the architecture’s proficiency in uniting motion data with classic graphic information. Experimentations operated on prominent datasets reveal that motion dynamics significantly enhance recognition exactness, mainly under challenging circumstances like occlusions, inconsistent viewpoints, and complicated actions. This study highlights how motion-informed CNN architectures can enhance HAR classification and open new avenues for multimodal action recognition research.