Helmet use detection of tracked motorcycles using CNN-based multi-task learning.

Lin, Hanhe; Deng, Jeremiah D.; Albers, Deike; Siebert, Felix Wilhelm

Abstract

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilities to track individual motorcycles through multiple frames, or to distinguish drivers from passengers in helmet use. Furthermore, datasets used to develop approaches are limited in terms of traffic environments and traffic density variations. In this paper, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, and register rider specific helmet use. We further release the HELMET dataset, which includes 91,000 annotated frames of 10,006 individual motorcycles from 12 observation sites in Myanmar. Along with the dataset, we introduce an evaluation metric for helmet use and rider detection accuracy, which can be used as a benchmark for evaluating future detection approaches. We show that the use of MTL for concurrent visual similarity learning and helmet use classification improves the efficiency of our approach compared to earlier studies, allowing a processing speed of more than 8 FPS on consumer hardware, and a weighted average F-measure of 67.3% for detecting the number of riders and helmet use of tracked motorcycles. Our work demonstrates the capability of deep learning as a highly accurate and resource efficient approach to collect critical road safety related data.

Citation

LIN, H., DENG, J.D., ALBERS, D. and SIEBERT, F.W. 2020. Helmet use detection of tracked motorcycles using CNN-based multi-task learning. IEEE access [online], 8, pages 162073-162084. Available from: https://doi.org/10.1109/access.2020.3021357

• Journal Article Type: Article
• Acceptance Date: Aug 22, 2020
• Online Publication Date: Sep 2, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: May 3, 2022
• Publicly Available Date: May 3, 2022
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 162073-162084
• DOI: https://doi.org/10.1109/access.2020.3021357
• Keywords: Motorcycles; Roads; Machine learning; Observers; Object detection; Measurement
• Public URL: https://rgu-repository.worktribe.com/output/1580715