Advanced Pedestrian Dataset Augmentation for Autonomous Driving
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00335023" target="_blank" >RIV/68407700:21730/19:00335023 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCVW.2019.00290" target="_blank" >https://doi.org/10.1109/ICCVW.2019.00290</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCVW.2019.00290" target="_blank" >10.1109/ICCVW.2019.00290</a>
Alternative languages
Result language
angličtina
Original language name
Advanced Pedestrian Dataset Augmentation for Autonomous Driving
Original language description
Having the ability of generating people images in arbitrary, yet admissible, pose is a crucial prerequisite for Autonomous Driving applications. Firstly, because the existing datasets are quite limited in the human pose variation and appearance. Secondly, because the strict safety requirements call for the ability of validation on rare situations. Generating realistically looking people images is very challenging problem due to various transformations of individual body parts self occlusions etc. We propose a novel approach for person image generation. Our approach allows generating people images in a required pose, indicated by specific pose keypoints and deals with occlusions. We build on top of the recent prevailing success of Generative Adversarial Networks. Our contributions comprise of the networks architecture, as well as the novel loss terms specically designed to generate visually appealing pedestrians tting the surrounding environment well.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 IEEE International Conference on Computer Vision Workshops (ICCVW 2019)
ISBN
—
ISSN
2473-9944
e-ISSN
2473-9944
Number of pages
6
Pages from-to
2367-2372
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Seoul
Event date
Oct 27, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—