Publication: Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review
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2021-10
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IEEE
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Abstract
Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area.
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Vulnerable road users (VRU), Vehicle detection, Tracking, Convolutional Neural Networks (CNNS), Deep learning
Bibliographic citation
Espinosa, J. E., Velastin, S. A. & Branch, J. W. (2021). Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review. IEEE Transactions on Intelligent Transportation Systems, 22(10), pp. 6115–6130.