Learning to See Through Haze: Radar-based Human Detection for Adverse Weather Conditions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00334757" target="_blank" >RIV/68407700:21230/19:00334757 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8870954" target="_blank" >https://ieeexplore.ieee.org/document/8870954</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECMR.2019.8870954" target="_blank" >10.1109/ECMR.2019.8870954</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning to See Through Haze: Radar-based Human Detection for Adverse Weather Conditions

Popis výsledku v původním jazyce

In this paper, we present a lifelong-learning multisensor system for pedestrian detection in adverse weather conditions. The proposed method combines two people detection pipelines which process data provided by a lidar and an ultrawideband radar. The outputs of these pipelines are combined not only by means of adaptive sensor fusion, but they can also be used to help one another learn. In particular, the lidar-based detector provides labels to the incoming radar data, efficiently training the radar data classifier. In several experiments, we show that the proposed learning-fusion not only results in a gradual improvement of the system performance during routine operation, but also efficiently deals with lidar detection failures caused by thick fog conditions.

Název v anglickém jazyce

Learning to See Through Haze: Radar-based Human Detection for Adverse Weather Conditions

Popis výsledku anglicky

In this paper, we present a lifelong-learning multisensor system for pedestrian detection in adverse weather conditions. The proposed method combines two people detection pipelines which process data provided by a lidar and an ultrawideband radar. The outputs of these pipelines are combined not only by means of adaptive sensor fusion, but they can also be used to help one another learn. In particular, the lidar-based detector provides labels to the incoming radar data, efficiently training the radar data classifier. In several experiments, we show that the proposed learning-fusion not only results in a gradual improvement of the system performance during routine operation, but also efficiently deals with lidar detection failures caused by thick fog conditions.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of European Conference on Mobile Robots

ISBN

978-1-7281-3606-6

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

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Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

4. 8. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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