Classifying Cycling Hazards in Egocentric Data

TL;DR

This paper proposes creating and annotating an egocentric video dataset of hazardous cycling situations to improve safety, infrastructure analysis, and real-time hazard warnings for cyclists.

Contribution

It introduces a new egocentric dataset focused on cycling hazards, enabling better hazard detection and infrastructure assessment.

Findings

Dataset creation and annotation plan for cycling hazards

Potential applications in hazard detection and infrastructure analysis

Facilitates real-time hazard warning development

Abstract

This proposal is for the creation and annotation of an egocentric video data set of hazardous cycling situations. The resulting data set will facilitate projects to improve the safety and experience of cyclists. Since cyclists are highly sensitive to road surface conditions and hazards they require more detail about road conditions when navigating their route. Features such as tram tracks, cobblestones, gratings, and utility access points can pose hazards or uncomfortable riding conditions for their journeys. Possible uses for the data set are identifying existing hazards in cycling infrastructure for municipal authorities, real time hazard and surface condition warnings for cyclists, and the identification of conditions that cause cyclists to make sudden changes in their immediate route.