Real-Time Human Motion Behaviour Recognition Using Deep Learning Models

Abstract

Human motion behavior recognition (HMBR) is a critical research area in computer vision, enabling automated understanding of human activities through deep learning models. This field has witnessed rapid advancements due to the growing availability of large-scale motion datasets and computational improvements in deep learning architectures. Applications range from healthcare monitoring and security surveillance to sports analytics and human-computer interaction (HCI). Traditional handcrafted feature extraction techniques have been largely replaced by deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer-based architectures. This paper presents a comprehensive study on real-time HMBR using deep learning, covering data acquisition, preprocessing, model architectures, optimization techniques, and real-time deployment strategies. Additionally, challenges such as occlusion, noise, computational efficiency, and ethical concerns are discussed, along with future research directions in self-supervised learning, multimodal data fusion, and scalable real-world deployment.