Paper Abstract and Keywords |
Presentation |
2023-03-03 09:10
Unknown Object Segmentation by View Independent Scene Change Detection Li Jiaxin (Nagoya Univ.), Yasutomo Kawanishi (RIKEN), Daisuke Deguchi, Hiroshi Murase (Nagoya Univ.) PRMU2022-99 IBISML2022-106 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Exploring the indoor environment and finding unknown objects that appeared in a scene is important for scene understanding by a robot. While background subtraction is traditionally used for segmenting unknown object regions, it cannot be directly used for a moving camera on the robot. In this paper, we address a task called view-independent panoptic scene change detection, which is the task of segmenting unknown object regions by comparing two images from different viewpoints before and after the objects appear. We propose a method for segmenting unknown object regions by modeling each segmented known instance region as a background. For the background modeling, we propose a deep metric learning-based method. In addition, we create a new panoptic scene change detection dataset consisting of images taken from different camera viewpoints. Through experiments, we confirm that the proposed method can segment regions of unknown class objects; the deep-metric-learning-based method performs more accurately than a simple histogram-based method, achieving good performance on the change detection dataset. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Scene change detection / Unknown object / Background modeling / Deep metric learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 404, PRMU2022-99, pp. 211-216, March 2023. |
Paper # |
PRMU2022-99 |
Date of Issue |
2023-02-23 (PRMU, IBISML) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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PRMU2022-99 IBISML2022-106 |
Conference Information |
Committee |
PRMU IBISML IPSJ-CVIM |
Conference Date |
2023-03-02 - 2023-03-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Future University Hakodate |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2023-03-PRMU-IBISML-CVIM |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Unknown Object Segmentation by View Independent Scene Change Detection |
Sub Title (in English) |
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Keyword(1) |
Scene change detection |
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Unknown object |
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Background modeling |
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Deep metric learning |
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1st Author's Name |
Li Jiaxin |
1st Author's Affiliation |
Nagoya University (Nagoya Univ.) |
2nd Author's Name |
Yasutomo Kawanishi |
2nd Author's Affiliation |
RIKEN (RIKEN) |
3rd Author's Name |
Daisuke Deguchi |
3rd Author's Affiliation |
Nagoya University (Nagoya Univ.) |
4th Author's Name |
Hiroshi Murase |
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Nagoya University (Nagoya Univ.) |
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Speaker |
Author-1 |
Date Time |
2023-03-03 09:10:00 |
Presentation Time |
10 minutes |
Registration for |
PRMU |
Paper # |
PRMU2022-99, IBISML2022-106 |
Volume (vol) |
vol.122 |
Number (no) |
no.404(PRMU), no.405(IBISML) |
Page |
pp.211-216 |
#Pages |
6 |
Date of Issue |
2023-02-23 (PRMU, IBISML) |
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