Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos. They are both active research topics in computer vision community, which have attracted considerable attention from academia and industry. They are also the precondition for intelligent interaction and human-computer cooperation, and they help the machine perceive the external environment. In the past decade, tremendous progress has been made in the field, especially after the emergence of deep learning technologies. Hence, it is necessary to make a comprehensive review of recent developments. In this paper, firstly, we attempt to present the background, and then discuss research progresses. Secondly, we introduce datasets, various typical feature representation methods, and explore advanced human action recognition and posture prediction algorithms. Finally, facing the challenges in the field, this paper puts forward the research focus, and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.

The development of autonomous driving has brought with it requirements for intelligence, safety, and stability. One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic, complex, and uncertain environments. Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies. Specifically, vehicles need to detect pedestrians, recognize their limb movements, and understand the meaning of their actions before making appropriate decisions in response. In this survey, we present a detailed description of the architecture for pedestrian action recognition in autonomous driving, and compare the existing mainstream pedestrian action recognition techniques. We also introduce several commonly used datasets used in pedestrian motion recognition. Finally, we present several suggestions for future research directions.