Survey of pedestrian action recognition techniques for autonomous driving
Li Chen, Nan Ma, Patrick Wang, Jiahong Li, Pengfei Wang, Guilin Pang, Xiaojun Shi
Abstract
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.