Paper Abstract and Keywords |
Presentation |
2022-10-21 15:55
Fine-grained Action Recognition Using Hand Trajectory Features for First-Person Cooking Videos Sota Miyamoto (Tokyo Tech), Yagi Takuma (UTokyo), Ushiku Yoshitaka, Atsushi Hashimoto (OSX), Nakamasa Inoue (Tokyo Tech) PRMU2022-29 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Action recognition in kitchen environments is a crucial topic in computer vision and robotics
with application toward fully automatic cooking. In this paper, we propose a method of fine-grained action recognition using
hand trajectory features. Our method extracts seven low-level features of hand trajectories and feeds them into a Transformer to classify fine-grained actions. In experiments, we annotated fine-grained labels of cutting
methods such as slice and mince on EPIC-KITCHENS and Ego4D datasets for evaluating our method. We showed that hand
trajectory features improve the recognition accuracy over the baseline method using RGB, optical flow, and audio features. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Fine-grained action recognition / Cooking videos / Hand-trajectory features / Multimodal deep learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 223, PRMU2022-29, pp. 41-46, Oct. 2022. |
Paper # |
PRMU2022-29 |
Date of Issue |
2022-10-14 (PRMU) |
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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PRMU2022-29 |
Conference Information |
Committee |
PRMU |
Conference Date |
2022-10-21 - 2022-10-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Miraikan - The National Museum of Emerging Science and Innovation |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Recognition and understanding related to people |
Paper Information |
Registration To |
PRMU |
Conference Code |
2022-10-PRMU |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Fine-grained Action Recognition Using Hand Trajectory Features for First-Person Cooking Videos |
Sub Title (in English) |
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Keyword(1) |
Fine-grained action recognition |
Keyword(2) |
Cooking videos |
Keyword(3) |
Hand-trajectory features |
Keyword(4) |
Multimodal deep learning |
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1st Author's Name |
Sota Miyamoto |
1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
2nd Author's Name |
Yagi Takuma |
2nd Author's Affiliation |
The University of Tokyo (UTokyo) |
3rd Author's Name |
Ushiku Yoshitaka |
3rd Author's Affiliation |
OMRON SINIC X (OSX) |
4th Author's Name |
Atsushi Hashimoto |
4th Author's Affiliation |
OMRON SINIC X (OSX) |
5th Author's Name |
Nakamasa Inoue |
5th Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
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Speaker |
Author-1 |
Date Time |
2022-10-21 15:55:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2022-29 |
Volume (vol) |
vol.122 |
Number (no) |
no.223 |
Page |
pp.41-46 |
#Pages |
6 |
Date of Issue |
2022-10-14 (PRMU) |
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