A crosswalk pedestrian recognition system by using deep learning and zebra-crossing recognition techniques

Pedestrian detection is essential for improving pedestrian safety in an intelligent traffic system. The efficiency of the system is affected by real-time processing and the error rate of detection. These concerns have not been completely addressed in previous studies. Therefore, this study proposes a real-time pedestrian recognition system that ensures high accuracy by using a deep learning classifier and zebra-crossing recognition techniques. The proposed system was designed to improve pedestrian safety and reduce accidents at intersections. Environmental feature vectors were first used to detect zebra crossings and to determine crossing areas. An adaptive mapping technique was then used to map the pedestrian waiting area based on the crossing area. A dual camera mechanism was used to maintain detection accuracy and improve system fault tolerance. Finally, the you-only-look-once model was used to recognize pedestrians at intersections. A system prototype was implemented to verify the feasibility of the proposed system. The results revealed that the proposed scheme outperforms the conventional histogram of oriented gradients and Haarcascade schemes.