Exploring Human Activity Recognition Systems: Insights from Computer Vision Approaches

Human Activity Recognition (HAR) through computer vision techniques has emerged as a pivotal research domain within computer science, particularly in sectors such as healthcare, security, and intelligent environments. This paper provides an extensive review of cutting-edge methodologies for identifying human activities from video and image data. Emphasis is placed on recent developments in deep learning, notably the application of Convolutional Neural Networks (CNNs), 3D CNNs, and Transformer-based models for action recognition. We examine a variety of datasets, benchmarks, and associated challenges, while also introducing an improved methodology that combines spatial and temporal feature learning. The findings indicate that hybrid models that integrate CNNs and Transformers surpass conventional methods, delivering enhanced accuracy and resilience. Lastly, the paper addresses existing limitations and proposes directions for future research, particularly in the realms of real-time HAR and the integration of multimodal sensors.