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Paper Abstract and Keywords
Presentation 2021-12-16 10:30
Real-world Anomaly Detection by Integrating Long-Term and Short-Term Classifications
Yudai Watanabe, Makoto Okabe (Shizuoka Univ.), Yasunori Harada, Yasuhiko Naoji (Chubu Electric Power Co., Inc.) PRMU2021-27
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a model to determine whether a video is normal or abnormal by using all the segments obtained from the video as input, and introducing a self-attention mechanism to analyze all the input segments and extract features that are important for determining normal/abnormal. The proposed method does not require MIL because it learns per video instead of per segment. During inference, the target video is divided into multiple short videos, and the proposed method is applied to each divided video to detect normal/abnormal per short time. We evaluated the frame-level detection accuracy of the proposed method on two benchmark datasets, and found that the proposed method can achieve the comparable accuracy as the state-of-the-art methods. In addition, the accuracy can be further improved by ensembling with other methods. We show that by averaging the results of the long-term and short-term detectors, we can achieve better detection accuracy than state-of-the-art methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Video anomaly detection / surveillance camera / weakly supervised learning / ensemble / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-27, pp. 19-24, Dec. 2021.
Paper # PRMU2021-27 
Date of Issue 2021-12-09 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee PRMU  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2021-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Real-world Anomaly Detection by Integrating Long-Term and Short-Term Classifications 
Sub Title (in English)  
Keyword(1) Video anomaly detection  
Keyword(2) surveillance camera  
Keyword(3) weakly supervised learning  
Keyword(4) ensemble  
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1st Author's Name Yudai Watanabe  
1st Author's Affiliation Shizuoka University (Shizuoka Univ.)
2nd Author's Name Makoto Okabe  
2nd Author's Affiliation Shizuoka University (Shizuoka Univ.)
3rd Author's Name Yasunori Harada  
3rd Author's Affiliation Chubu Electric Power Co., Inc. (Chubu Electric Power Co., Inc.)
4th Author's Name Yasuhiko Naoji  
4th Author's Affiliation Chubu Electric Power Co., Inc. (Chubu Electric Power Co., Inc.)
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Speaker Author-1 
Date Time 2021-12-16 10:30:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-27 
Volume (vol) vol.121 
Number (no) no.304 
Page pp.19-24 
#Pages
Date of Issue 2021-12-09 (PRMU) 


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