Artificial Dummies for Urban Dataset Augmentation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00350322" target="_blank" >RIV/68407700:21730/21:00350322 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1609/aaai.v35i3.16373" target="_blank" >https://doi.org/10.1609/aaai.v35i3.16373</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v35i3.16373" target="_blank" >10.1609/aaai.v35i3.16373</a>

Result language

angličtina

Original language name

Artificial Dummies for Urban Dataset Augmentation

Original language description

Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unlikely to happen. The strict safety requirements in assisted and autonomous driving applications call for an extra high detection accuracy also in these rare situations. Having the ability to generate people images in arbitrary poses, with arbitrary appearances and embedded in different background scenes with varying illumination and weather conditions, is a crucial component for the development and testing of such applications. The contributions of this paper are three-fold. First, we describe an augmentation method for the controlled synthesis of urban scenes containing people, thus producing rare or never-seen situations. This is achieved with a data generator (called DummyNet) with disentangled control of the pose, the appearance, and the target background scene. Second, the proposed generator relies on novel network architecture and associated loss that takes into account the segmentation of the foreground person and its composition into the background scene. Finally, we demonstrate that the data generated by our DummyNet improve the performance of several existing person detectors across various datasets as well as in challenging situations, such as night-time conditions, where only a limited amount of training data is available. In the setup with only day-time data available, we improve the night-time detector by 17% log-average miss rate over the detector trained with the day-time data only.

Czech name

—

Czech description

—

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)

Project

<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-866-4

ISSN

2159-5399

e-ISSN

2374-3468

Number of pages

9

Pages from-to

2692-2700

Publisher name

Association for the Advancement of Artificial Intelligence (AAAI)

Place of publication

Palo Alto, California

Event location

Virtual Conference

Event date

Feb 2, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000680423502088