Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept-Reference-Cited by-同舟云学术

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Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept

Published:2024-06-02 Issue: Volume:8 Page:2117-2124
ISSN:
Container-title:2024 IEEE Intelligent Vehicles Symposium (IV)
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Author:

Ewecker Lukas¹,Wagner Niklas²,Brühl Tim¹,Schwager Robin¹,Sohn Tin Stribor¹,Engelsberger Alexander³,Ravichandran Jensun³,Stage Hanno⁴,Langner Jacob⁴,Saralajew Sascha⁵

Affiliation:

1. Porsche AG,Weissach,Germany

2. Ulm University,Germany

3. University of Applied Science Mittweida,Germany

4. FZI Research Center for Information Technology,Karlsruhe,Germany

5. NEC Laboratories Europe GmbH,Heidelberg,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587320/10588370/10588522.pdf?arnumber=10588522

Reference31 articles.

1. Provident Detection of Vehicles at Night

2. A Dataset for Provident Vehicle Detection at Night

3. Combining visual saliency methods and sparse keypoint annotations to providently detect vehicles at night;Ewecker,2022

4. Detecting vehicles in the dark in urban environments - A human benchmark

5. Quanti.us: a tool for rapid, flexible, crowd-based annotation of images

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