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Camera-based position analysis system for cyclists ordering in bicycle swarms

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dc.contributor.authorYadav, Vemburaj Chockalingam
dc.contributor.authorPagani, Alain
dc.contributor.authorStricker, Didier
dc.date.accessioned2023-08-24T06:24:25Z
dc.date.available2023-08-24T06:24:25Z
dc.date.issued2023
dc.description.abstractactivity. To offer enhanced digital services for swarm cycling, it is essential to obtain real-time information about the position of each cyclist within the swarm. While GNSS (Global Navigation Satellite Systems) signals such as GPS, Galileo or GLONASS may not provide precise positioning in such scenarios, this paper proposes a novel approach to address this challenge. By equipping each bicycle with a backward-facing camera and leveraging computer vision and deep learning methodologies, we can achieve the absolute ordering of bicyclists in real-time. This position paper outlines a comprehensive framework that utilizes object detection, monocular depth estimation, and object tracking models to process camera information and obtain accurate positioning within the swarm. The proposed solution also enables the detection of overtakes between cyclists, adding an additional layer of information to enhance the overall swarm cycling experience.de
dc.identifier.doi10.18420/muc2023-mci-ws03-481
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/42072
dc.publisherGI
dc.relation.ispartofMensch und Computer 2023 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.titleCamera-based position analysis system for cyclists ordering in bicycle swarmsde
dc.typeText/Workshop Paper
gi.conference.date3.-6. September 2023
gi.conference.locationRapperswil
gi.conference.sessiontitleMCI-WS03: Workshop on Smart Urban Micromobility

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