PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

Bor-Shiun Wang, Ping-Yang Chen, Yi-Kuan Hsieh, Jun-Wei Hsieh, Ming-Ching Chang, JiaXin He, Shin-You Teng, HaoYuan Yue, Yu-Chee Tseng

Abstract: We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://github.com/NYCU-AICVLab/AICITY_2023_Track5.