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.
Highlights
Nowadays, drivers’ adherence to traffic laws is mainly monitored and enforced by traffic police officers through direct observation
For the element of detection of helmet use class, i.e. the registration of rider number, position, and rider specific helmet use, we achieve an accuracy of 80.6% on a frame based level
Our results show a weighted F-measure of 67.3% for the helmet use detection of tracked motorcycles, showing that our approach can be used to generate reliable motorcycle, rider number, and position specific helmet use estimates
Summary
Drivers’ adherence to traffic laws is mainly monitored and enforced by traffic police officers through direct observation. Automated approaches need to show accuracy for more than one road environment While these four basic elements of motorcycle helmet use observation come naturally to humans observers, existing automated detection approaches, either do not include all four elements or have low performance on some of them (see Section II). We present a deep learning based automatic detection approach that contains all four basic elements of human-observer helmet use registration, i.e. detection, tracking, rider differentiation, and site-diversity. The proposed work builds on and extends a previous approach for frame-based helmet use detection [10], which did not include tracking of motorcycles and in which the dataset was not made public. Our main contributions are twofold: We propose a comprehensive CNN-based approach for helmet use detection of tracked motorcycles, containing all basic elements utilized by human observers. The dataset, together with the source code for performance evaluation, are available in [17]
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