Using Computer Vision to Collect Information on Cycling and Hiking Trails Users

Joaquim Miguel, Pedro Mendonça, Agnelo Quelhas, João M. L. P. Caldeira () and Vasco N. G. J. Soares
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Joaquim Miguel: Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral n° 12, 6000-084 Castelo Branco, Portugal
Pedro Mendonça: Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral n° 12, 6000-084 Castelo Branco, Portugal
Agnelo Quelhas: Direção Geral da Educação/ERTE, Av. 24 de Julho n.º 140-5.º piso, 1399-025 Lisboa, Portugal
João M. L. P. Caldeira: Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral n° 12, 6000-084 Castelo Branco, Portugal
Vasco N. G. J. Soares: Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral n° 12, 6000-084 Castelo Branco, Portugal

Future Internet, 2024, vol. 16, issue 3, 1-32

Abstract: Hiking and cycling have become popular activities for promoting well-being and physical activity. Portugal has been investing in hiking and cycling trail infrastructures to boost sustainable tourism. However, the lack of reliable data on the use of these trails means that the times of greatest affluence or the type of user who makes the most use of them are not recorded. These data are of the utmost importance to the managing bodies, with which they can adjust their actions to improve the management, maintenance, promotion, and use of the infrastructures for which they are responsible. The aim of this work is to present a review study on projects, techniques, and methods that can be used to identify and count the different types of users on these trails. The most promising computer vision techniques are identified and described: YOLOv3-Tiny, MobileNet-SSD V2, and FasterRCNN with ResNet-50. Their performance is evaluated and compared. The results observed can be very useful for proposing future prototypes. The challenges, future directions, and research opportunities are also discussed.

Keywords: hiking trails; cycling trails; computer vision; convolutional neural networks; state of the art; performance evaluation; YOLOv3-Tiny; MobileNet-SSD V2; FasterRCNN with ResNet-50 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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