A Computer Vision-Based Algorithm for Detecting Vehicle Yielding to Pedestrians

Yanqi Wan (), Yaqi Xu, Yi Xu, Heyi Wang, Jian Wang and Mingzheng Liu
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Yanqi Wan: School of Journalism and Communication, Jilin University, Changchun 130012, China
Yaqi Xu: School of Computer Science, Beijing University of Post and Telecommunications, Beijing 100876, China
Yi Xu: School of Public Administration, Jilin University, Changchun 130012, China
Heyi Wang: School of Resources and Environment, Jilin Agricultural University, Changchun 130118, China
Jian Wang: School of Computer Science and Technology, Jilin University, Changchun 130012, China
Mingzheng Liu: School of Journalism and Communication, Jilin University, Changchun 130012, China

Sustainability, 2023, vol. 15, issue 22, 1-24

Abstract: Computer vision has made remarkable progress in traffic surveillance, but determining whether a motor vehicle yields to pedestrians still requires considerable human effort. This study proposes an automated method for detecting whether a vehicle yields to pedestrians in intelligent transportation systems. The method employs a target-tracking algorithm that uses feature maps and license plate IDs to track the motion of relevant elements in the camera’s field of view. By analyzing the positions of motor vehicles and pedestrians over time, we predict the warning points of pedestrians and hazardous areas in front of vehicles to determine whether the vehicles yield to pedestrians. Extensive experiments are conducted on the MOT16 dataset, real traffic street scene video dataset, and a Unity3D virtual simulation scene dataset combined with SUMO, which demonstrating the superiority of this tracking algorithms. Compared to the current state-of-the-art methods, this method demonstrates significant improvements in processing speed without compromising accuracy. Specifically, this approach substantially outperforms in operational efficiency, thus catering aptly to real-time recognition requirements. This meticulous experimentation and evaluations reveal a commendable reduction in ID switches, enhancing the reliability of violation attributions to the correct vehicles. Such enhancement is crucial in practical urban settings characterized by dynamic interactions and variable conditions. This approach can be applied in various weather, time, and road conditions, achieving high predictive accuracy and interpretability in detecting vehicle–pedestrian interactions. This advanced algorithm illuminates the viable pathways for integrating technological innovation and sustainability, paving the way for more resilient and intelligent urban ecosystems.

Keywords: intelligent transportation; computer vision; object tracking; behavior detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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