Cheap Rendering vs. Costly Annotation: Rendered Omnidirectional Dataset of Vehicles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU112006" target="_blank" >RIV/00216305:26230/14:PU112006 - isvavai.cz</a>

Výsledek na webu

<a href="http://medusa.fit.vutbr.cz/SynthCars/" target="_blank" >http://medusa.fit.vutbr.cz/SynthCars/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2643188.2643191" target="_blank" >10.1145/2643188.2643191</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cheap Rendering vs. Costly Annotation: Rendered Omnidirectional Dataset of Vehicles

Popis výsledku v původním jazyce

Detection of vehicles in traffic surveillance needs good and large training datasets in order to achieve competitive detection rates. We are showing an approach to automatic synthesis of custom datasets, simulating various major influences: viewpoint, camera parameters, sunlight, surrounding environment, etc. Our goal is to create a competitive vehicle detector which "has not seen a real car before." We are using Blender as the modeling and rendering engine. A suitable scene graph accompanied by a set of scripts was created, that allows simple configuration of the synthesized dataset. The generator is also capable of storing rich set of metadata that are used as annotations of the synthesized images. We synthesized several experimental datasets, evaluated their statistical properties, as compared to real-life datasets. Most importantly, we trained a detector on the synthetic data. Its detection performance is comparable to a detector trained on state-of-the-art real-life dataset. Synthesis of a dataset of 10,000 images takes only several hours, which is much more efficient, compared to manual annotation, let aside the possibility of human error in annotation.

Název v anglickém jazyce

Cheap Rendering vs. Costly Annotation: Rendered Omnidirectional Dataset of Vehicles

Popis výsledku anglicky

Detection of vehicles in traffic surveillance needs good and large training datasets in order to achieve competitive detection rates. We are showing an approach to automatic synthesis of custom datasets, simulating various major influences: viewpoint, camera parameters, sunlight, surrounding environment, etc. Our goal is to create a competitive vehicle detector which "has not seen a real car before." We are using Blender as the modeling and rendering engine. A suitable scene graph accompanied by a set of scripts was created, that allows simple configuration of the synthesized dataset. The generator is also capable of storing rich set of metadata that are used as annotations of the synthesized images. We synthesized several experimental datasets, evaluated their statistical properties, as compared to real-life datasets. Most importantly, we trained a detector on the synthetic data. Its detection performance is comparable to a detector trained on state-of-the-art real-life dataset. Synthesis of a dataset of 10,000 images takes only several hours, which is much more efficient, compared to manual annotation, let aside the possibility of human error in annotation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Spring Conference on Computer Graphics

ISBN

978-80-223-3601-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

105-112

Název nakladatele

Comenius University in Bratislava

Místo vydání

Smolenice

Místo konání akce

Smolenice

Datum konání akce

27. 5. 2014

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—