Current Advances on Deep Learning-based Human Action Recognition from Videos: a Survey

Abstract

Human action recognition (HAR) from RGB videos is essential and challenging in the computer vision field due to its wide range of real-world applications in fields of human behaviour analysis, human-computer interactions, robotics and surveillance etc. Since the breakthrough and fast development of deep learning technology, the performance of HAR based on deep neural networks has been significantly improved in this decade. In this survey, we discuss the growing use of deep learning for HAR, such as representative two-stream and 3D CNNs, and particularly highlight most recent success achieved by using attention and transformers. We will provide our perspective on the new trend of designing innovative deep learning methods. In addition, we also present popular HAR datasets developed in recent years and benchmark accuracy achieved by current advancement in deep learning. This draws research attention to the challenges of HAR by identifying performance gaps when applying the deep learning methods on large HAR datasets. Further, this survey sheds light on the development of new methods and facilitates qualitative comparison with state of the art.