Improving Multi-view Object Recognition by Detecting Changes in Point Clouds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU121600" target="_blank" >RIV/00216305:26230/16:PU121600 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7850045/" target="_blank" >http://ieeexplore.ieee.org/document/7850045/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SSCI.2016.7850045" target="_blank" >10.1109/SSCI.2016.7850045</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Multi-view Object Recognition by Detecting Changes in Point Clouds

Popis výsledku v původním jazyce

This paper proposes the use of change detection in a multi-view object recognition system in order to improve its flexibility and effectiveness in dynamic environments. Multi-view recognition approaches are essential to overcome problems related to clutter, occlusion or camera noise, but the existing systems usually assume a static environment. The presence of dynamic objects raises another issue - the inconsistencies introduced to the internal scene model. We show that by incorporating the change detection and correction of the inherent scene inconsistencies, we can reduce false positive detections by 70% in average for moving objects when tested on the publicly available TUW dataset. To reduce time required for verifying a large set of accumulated object pose hypotheses, we further integrate a clustering approach into the original multi-view object recognition system and show that this reduces computation time by approximately 16%.

Název v anglickém jazyce

Improving Multi-view Object Recognition by Detecting Changes in Point Clouds

Popis výsledku anglicky

This paper proposes the use of change detection in a multi-view object recognition system in order to improve its flexibility and effectiveness in dynamic environments. Multi-view recognition approaches are essential to overcome problems related to clutter, occlusion or camera noise, but the existing systems usually assume a static environment. The presence of dynamic objects raises another issue - the inconsistencies introduced to the internal scene model. We show that by incorporating the change detection and correction of the inherent scene inconsistencies, we can reduce false positive detections by 70% in average for moving objects when tested on the publicly available TUW dataset. To reduce time required for verifying a large set of accumulated object pose hypotheses, we further integrate a clustering approach into the original multi-view object recognition system and show that this reduces computation time by approximately 16%.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/TE01020415" target="_blank" >TE01020415: Centrum kompetence ve zpracování vizuálních informací (V3C - Visual Computing Competence Center)</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing

ISBN

978-1-5090-4239-5

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE Computer Society

Místo vydání

Atény

Místo konání akce

Athens

Datum konání akce

6. 12. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000400488301086