Research Article
A Deep Learning-Based Method for Drivers’ Shoe-Wearing Recognition
@INPROCEEDINGS{10.1007/978-3-031-34899-0_11, author={Baoyue Hu and Xing Hu}, title={A Deep Learning-Based Method for Drivers’ Shoe-Wearing Recognition}, proceedings={Sensor Systems and Software. 13th EAI International Conference, S-Cube 2022, Dalian, China, December 7-9, 2022, Proceedings}, proceedings_a={S-CUBE}, year={2023}, month={6}, keywords={YOLO v4 Deep Learning Computer Vision Technology Shoe-Wearing Recognition}, doi={10.1007/978-3-031-34899-0_11} }- Baoyue Hu
Xing Hu
Year: 2023
A Deep Learning-Based Method for Drivers’ Shoe-Wearing Recognition
S-CUBE
Springer
DOI: 10.1007/978-3-031-34899-0_11
Abstract
What types of shoes that the driver should be wear on driving is under the clear regulation. Non-standard shoe-wearing such as wearing high-heeled shoes, platform shoes, slippers or with bare feet will bring great safety risks and may lead to traffic accidents. According to statistics by traffic department, many traffic accidents are caused by irregular shoe-wearing. Although computer vision has been applied for monitoring driver’s behavior in many aspects, such as driver’s face, eye, and hand, there is still no computer vision-based method or hardware device on the vehicle that to monitor the driver’s shoe-wearing. Therefore, if the driver’s illegal shoe-wearing behavior can be identified before driving, it can play an important role in reducing the incidence of traffic accident. The main difficulties in drivers’ shoe-wearing detection lie in the diversity of shoe types, the variety of foot postures, and the complexity of the detection environment. Therefore, the traditional computer vision method will be high error detection rate in such scenes. In this paper, a deep learning-based method for detecting abnormal shoe-wearing of drivers before driving is proposed. Different models such as SVM, Fast-RCNN, Faster-RCNN, YOLO v2, YOLO v3, YOLO v4 are used to identified drivers’ shoe-wearing. To the best of our knowledge this is the first work that using the computer vision technology for automatic monitoring on drivers’ shoe-wearing. The experimental results show the proposed method can identify the drivers’ shoe wearing effectively and efficiently. It has high application value for improving traffic safety.
