TY - UNPB
T1 - Classifying Cycling Hazards in Egocentric Data
T2 - Computer Vision and Pattern Recognition (cs.CV)
AU - Haebich, Jayson
AU - Sandor, Christian
AU - Cassinelli, Alvaro
PY - 2021/3/15
Y1 - 2021/3/15
N2 - 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.
AB - 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.
UR - https://www.semanticscholar.org/paper/Classifying-Cycling-Hazards-in-Egocentric-Data-Haebich-Sandor/a0a89ddddfdedbfacb67b7ea4d2290d10b50335b
M3 - Working paper
VL - arXiv:2103.08102
T3 - arXiv
BT - Classifying Cycling Hazards in Egocentric Data
ER -