Joaquim Miguel, Pedro Mendonça, Agnelo Quelhas, J. M. L. P. Caldeira, V. N. Soares
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引用次数: 0
摘要
徒步旅行和骑自行车作为促进身心健康和体育锻炼的方式,越来越受到人们的欢迎。葡萄牙政府也注意到了这一点,投资建设了基础设施,以支持这些活动,促进可持续发展和以自然为基础的旅游业。然而,由于缺乏有关这些基础设施使用情况的可靠数据,我们无法记录这些设施的使用率和最常使用的用户类型。这些信息对于负责管理、维护、推广和使用这些基础设施的部门非常重要。从这个意义上说,本研究以同一作者之前的一项研究为基础,该研究认为计算机视觉是一种合适的技术,可用于识别和统计自行车和徒步路线的不同类型用户。通过性能测试得出的结论是,YOLOv3-Tiny 卷积神经网络在解决这一问题方面具有巨大潜力。基于这一结果,本文介绍了原型验证器的提案和实施。它基于 Raspberry Pi 4 平台和 YOLOv3-Tiny,YOLOv3-Tiny 负责检测和分类用户类型。用户智能手机上的一个应用程序实现了机会主义网络的概念,允许在没有端到端连接的情况下长期收集信息。然后,可以在一个在线平台上查询这些汇总信息。对原型进行了验证和功能测试,证明这是一个可行的低成本解决方案。
The Development of a Prototype Solution for Collecting Information on Cycling and Hiking Trail Users
Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable data on the use of these infrastructures prevents us from recording attendance rates and the most frequent types of users. This information is important for the authorities responsible for managing, maintaining, promoting and using these infrastructures. In this sense, this study builds on a previous study by the same authors which identified computer vision as a suitable technology to identify and count different types of users of cycling and hiking routes. The performance tests carried out led to the conclusion that the YOLOv3-Tiny convolutional neural network has great potential for solving this problem. Based on this result, this paper describes the proposal and implementation of a prototype demonstrator. It is based on a Raspberry Pi 4 platform with YOLOv3-Tiny, which is responsible for detecting and classifying user types. An application available on users’ smartphones implements the concept of opportunistic networks, allowing information to be collected over time, in scenarios where there is no end-to-end connectivity. This aggregated information can then be consulted on an online platform. The prototype was subjected to validation and functional tests and proved to be a viable low-cost solution.