Boarding and Alighting Behavior Recognition Based on Human-vehicle Interaction Behavior Model

Boarding and alighting behavior is a typical human-vehicle interaction behavior. However, boarding and alighting passengers randomly on roadsides and in no-parking areas may cause traffic disorder and severe traffic casualties, so it needs to be detected in time to ensure efficient traffic management. Following the development and deployment of smart light poles, the boarding and alighting behavior of the entire road can be detected. In this study, a human-vehicle interaction behavior (HVIB) model was established using smart light pole monitoring videos, and a method of boarding and alighting behavior recognition was developed. The HVIB model consisted of a vehicle-motion state detection module and a human-vehicle relationship detection module. In the vehicle-motion state detection module, the YOLOv4 object detection model and the SORT tracking algorithm were used to obtain high confidence object information and extract vehicle spatiotemporal position features. In the human-vehicle relationship detection module, combined with the spatial position change and movement direction of the pedestrian relative to the vehicle, the spatiotemporal feature expression of human-vehicle relationship was formed. The spatiotemporal position characteristics of humans and vehicles in the video were calculated, and the vehicle motion state and the relationship between humans and vehicles were output based on the vehicle motion state function and the human-vehicle relationship function, respectively. Next, the boarding and alighting behaviors were recognized according to the definitions of different human-vehicle interaction behaviors. Actual data obtained from urban traffic scene videos were used to perform recognition experiments of different human-vehicle behaviors under different weather conditions (such as sunny, cloudy, and rainy). The experimental results show that the proposed method can work day and night. The recognition accuracy of the parking and the boarding and alighting behavior can exceed 90% and 87%, respectively, under various weather conditions during the day. The corresponding values under normal weather conditions at night are 82.5% and 77.5%. The detection speed exceeds 30 frames per second, satisfying the real-time requirements for practical applications. © 2021, Editorial Department of China Journal of Highway and Transport. All right reserved.