Computer Vision with Deep Learning for Human Activity Recognition: Features Representation

Deep learning (DL) using artificial neural networks has made remarkable progress, fueled by the utilization of powerful GPUs and the availability of copious online data. This advancement has led to computers becoming highly intelligent across various fields, with computer vision being a prominent area of research and development (R&D). Specifically, Human activity recognition plays a pivotal role in various applications, including healthcare monitoring, surveillance and security systems, and human–machine interfaces. However, challenges persist in unconstrained environments, including occlusions, variations in clothing, and background noise, making these tasks difficult to solve. This review article offers a succinct examination of deep learning algorithms, with a specific emphasis on convolutional neural networks (CNNs), which have been suggested as a solution to classical artificial intelligence problems. Furthermore, the paper delves into the notable outcomes and contributions of various methodologies explored in human activity classification through the utilization of DL techniques. In conclusion, the paper emphasizes the potential of a hybrid approach that combines convolutional and recurrent neural networks in future solutions for human action/activity recognition. By combining the strengths of CNNs in extracting spatial features and RNNs in capturing temporal dependencies, the hybrid CNN-RNN models hold promise in effectively analyzing video data, leading to improved accuracy in classifying human activities. Ongoing research aims to further enhance these hybrid models to tackle the challenges of unconstrained environments and advance the human activity recognition field.