Vision-based approach for predicting the probability of vehicle–pedestrian collisions at intersections (Englisch)
Freier Zugriff
- Neue Suche nach: Zhou, Zhuping
- Neue Suche nach: Peng, Yunlong
- Neue Suche nach: Cai, Yifei
- Neue Suche nach: Zhou, Zhuping
- Neue Suche nach: Peng, Yunlong
- Neue Suche nach: Cai, Yifei
In:
IET Intelligent Transport Systems
;
14
, 11
;
1447-1455
;
2020
- Aufsatz (Zeitschrift) / Elektronische Ressource
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Titel:Vision-based approach for predicting the probability of vehicle–pedestrian collisions at intersections
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Beteiligte:
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Erschienen in:IET Intelligent Transport Systems ; 14, 11 ; 1447-1455
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Verlag:
- Neue Suche nach: The Institution of Engineering and Technology
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Erscheinungsdatum:24.02.2020
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Format / Umfang:9 pages
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:vehicle-pedestrian collision probability , convolutional neural nets , optical flow , intelligent pedestrian signal timing , probability , computer vision , Logit model , pedestrians , convolutional neural networks , real-world video data , road traffic , tracking algorithm , collision avoidance , trajectories generation probability , object detection , background subtraction , video signal processing , real-time trajectories , evasive action failure patterns , road safety , road vehicles , object tracking , detection algorithm , intelligent transportation systems , vision-based approach , collision risk , perception-reaction failure patterns , collision prediction , traffic engineering computing , intersections , road accidents , image sequences , discrete choice probability model
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Datenquelle:
Metadata by IET is licensed under CC BY 3.0
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