Low Cost, High Performance Automatic Motorcycle Helmet Violation Detection

Aphinya Chairat, Matthew Dailey, Somphop Limsoonthrakul, Mongkol Ekpanyapong, Dharma Raj KC; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3560-3568

Abstract


Road fatality rates are very high, especially in developing and middle-income countries. One of the main causes of road fatalities is not using motorcycle helmets. Active law enforcement may help increase compliance, but ubiquitous enforcement requires many police officers and may cause traffic jams and safety issues. In this paper, we demonstrate the effectiveness of computer vision and machine learning methods to increase helmet compliance through automated helmet violation detection. The system detects riders and passengers not wearing helmets and consists of motorcyclist detection, helmet violation classification, and tracking. The architecture of the system comprises a single GPU server and multiple computational clients that cooperate to complete the task, with communication over HTTP. In a real-world test, the system is able to detect 97% of helmet violations with a 15% false alarm rate. The client-server architecture reduces cost by 20-30% compared to a baseline architecture.

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[bibtex]
@InProceedings{Chairat_2020_WACV,
author = {Chairat, Aphinya and Dailey, Matthew and Limsoonthrakul, Somphop and Ekpanyapong, Mongkol and KC, Dharma Raj},
title = {Low Cost, High Performance Automatic Motorcycle Helmet Violation Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}