Pedestrian and Ego-vehicle Trajectory Prediction from Monocular Camera
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354367" target="_blank" >RIV/68407700:21230/21:00354367 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR46437.2021.01007" target="_blank" >https://doi.org/10.1109/CVPR46437.2021.01007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR46437.2021.01007" target="_blank" >10.1109/CVPR46437.2021.01007</a>
Alternative languages
Result language
angličtina
Original language name
Pedestrian and Ego-vehicle Trajectory Prediction from Monocular Camera
Original language description
Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e.g. breaking) to take place. In this paper, we propose a new method to predict future position of pedestrians, with respect to a predicted future position of the ego-vehicle, thus giving a assistive/autonomous driving system sufficient time to respond. The method explicitly disentangles actual movement of pedestrians in real world from the ego-motion of the vehicle, using a future pose prediction network trained in self-supervised fashion, which allows the method to observe and predict the intrinsic pedestrian motion in a normalised view, that captures the same real-world location across multiple frames.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
978-1-6654-4509-2
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
9
Pages from-to
10199-10207
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Nashville
Event date
Jun 20, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000742075000020