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ZEMČÍK, T.
Original Title
Pedestrian Detector Domain Shift Robustness Evaluation, and Domain Shift Error Mitigation Proposal
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
This paper evaluates daytime to nighttime traffic image domain shift on Faster R-CNN and SSD based pedestrian and cyclist detectors. Daytime image trained detectors are applied on a newly compiled nighttime image dataset and their performance is evaluated against detectors trained on both daytime and nighttime images. Faster R-CNN based detectors proved relatively robust, but still clearly inferior to the models trained on nighttime images, the SSD based model proved noncompetitive. Approaches to the domain shift deterioration mitigation were proposed and future work outlined.
English abstract
Keywords
Object detection, Pedestrian detection, Cyclist detection, ADAS, AV, Faster R-CNN, SSD, Domain shift, Domain adaptation, Data augmentation
Key words in English
Authors
RIV year
2021
Released
27.04.2021
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Communication
Location
Brno
ISBN
978-80-214-5943-4
Book
Proceedings II of the 27th student EEICT selected papers
Edition
1
Pages from
181
Pages to
187
Pages count
7
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf
BibTex
@inproceedings{BUT164116, author="Tomáš {Zemčík}", title="Pedestrian Detector Domain Shift Robustness Evaluation, and Domain Shift Error Mitigation Proposal", booktitle="Proceedings II of the 27th student EEICT selected papers", year="2021", series="1", pages="181--187", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", doi="10.13164/eeict.2021.181", isbn="978-80-214-5943-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf" }