Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 10, Pages 6115-6130Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.2997084
Keywords
Motorcycles; Feature extraction; Head; Safety; Roads; Cameras; Shape; Vulnerable road users (VRU); motorcycle detection; vehicle detection; tracking; convolutional neural networks (CNNs); deep learning; computer vision
Categories
Funding
- Departamento Administrativo de Ciencia, Tecnologia e Innovacion (COLCIENCIAS) Project: Reduccion de Emisiones Vehiculares Mediante el Modelado y Gestion Optima de Trafico en Areas Metropolitanas, Caso Medellin, Area Metropolitana del Valle de Aburra [111874558167, CT 049-2017]
- Universidad Nacional de Colombia [HERMES 25374]
- NVIDIA Corporation
- Universidad Carlos III de Madrid
- European Union's Seventh Framework Programme for Research, Technological Development and Demonstration [600371]
- El Ministerio de Economia y Competitividad [COFUND2013-51509]
- Banco Santander
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Motorcycles are vulnerable road users in urban areas, and automatic video processing using Deep Learning theory shows potential in effectively detecting and tracking them. The paper reviews algorithms used for motorcycle detection and tracking, introduces a new dataset, discusses future challenges, and concludes with proposed future work in this evolving area.
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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