A Survey of Video Action Recognition Based on Deep Learning

Video Action Recognition (VAR) involves identifying and classifying human actions from video data. Deep Learning (DL) has revolutionised VAR, significantly enhancing its accuracy and efficiency. However, large-scale practical applications of VAR using DL remain limited, underscoring the need for further research and innovation. Thus, this survey provides a comprehensive overview of recent advancements in DL-based VAR. Specifically, we summarise the key DL architectures for VAR, including two-stream networks, 3D-CNNs, RNNs, LSTMs, and Attention Mechanisms, and analyse their strengths, limitations, and benchmark performances. The survey also explores the diverse applications of DL-based VAR, such as surveillance, human–computer interaction, sports analytics, healthcare, and education, while presenting a detailed summary of commonly used datasets and evaluation metrics. Moreover, critical challenges, such as computational demands and the need for robust temporal modelling, are identified, along with potential future directions. This paper is a valuable resource for researchers and practitioners striving to advance VAR using DL techniques by systematically presenting concepts, methodologies, and trends.