Spatio-Semantic ConvNet-Based Visual Place Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00337205" target="_blank" >RIV/68407700:21730/19:00337205 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ECMR.2019.8870948" target="_blank" >https://doi.org/10.1109/ECMR.2019.8870948</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECMR.2019.8870948" target="_blank" >10.1109/ECMR.2019.8870948</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spatio-Semantic ConvNet-Based Visual Place Recognition

Popis výsledku v původním jazyce

We present a Visual Place Recognition system that follows the two-stage format common to image retrieval pipelines. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the first stage of our method and given a query image of a place, a number of top candidate images is retrieved from a previously stored database of places. In the second stage, we propose an exhaustive comparison of the query image against these candidates by encoding semantic and spatial information in the form of CNN features. Results from our approach outperform by a large margin state-of-the-art visual place recognition methods on five of the most commonly used benchmark datasets. The performance gain is especially remarkable on the most challenging datasets, with more than a twofold recognition improvement with respect to the latest published work.

Název v anglickém jazyce

Spatio-Semantic ConvNet-Based Visual Place Recognition

Popis výsledku anglicky

We present a Visual Place Recognition system that follows the two-stage format common to image retrieval pipelines. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the first stage of our method and given a query image of a place, a number of top candidate images is retrieved from a previously stored database of places. In the second stage, we propose an exhaustive comparison of the query image against these candidates by encoding semantic and spatial information in the form of CNN features. Results from our approach outperform by a large margin state-of-the-art visual place recognition methods on five of the most commonly used benchmark datasets. The performance gain is especially remarkable on the most challenging datasets, with more than a twofold recognition improvement with respect to the latest published work.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>

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 the 2019 European Conference on Mobile Robots

ISBN

978-1-7281-3605-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

American Institute of Physics and Magnetic Society of the IEEE

Místo vydání

San Francisco

Místo konání akce

Prague

Datum konání akce

4. 8. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000558081900044