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Motion-Attentive Network for Detecting Abnormal Situationsin Surveillance Video

Published: 17 August 2020 Publication History

Abstract

Recently, numerous studies have utilized deep-learning-based approaches to detect anomalies in surveillance cameras. However, while several of these studies used motion features to detect abnormal situations, detection problems can arise due to the sparse information and irregular patterns in certain abnormal situations. We propose a means of preserving motion patterns in abnormal situations through a network called MA-Net, which solves representation problems caused by a loss of sparse information and irregular patterns. We show through experiments that the proposed method is superior to state-of-the-art methods.

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References

[1]
Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K Roy-Chowdhury, and Larry S Davis. 2016. Learning temporal regularity in video sequences. In Proceedings of the IEEE conference on computer vision and pattern recognition. 733–742.
[2]
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2117–2125.
[3]
Cewu Lu, Jianping Shi, and Jiaya Jia. 2013. Abnormal event detection at 150 fps in matlab. In Proceedings of the IEEE international conference on computer vision. 2720–2727.
[4]
Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik Sudderth, and Jan Kautz. 2019. A fusion approach for multi-frame optical flow estimation. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2077–2086.
[5]
Waqas Sultani, Chen Chen, and Mubarak Shah. 2018. Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6479–6488.
[6]
Yi Zhu and Shawn Newsam. 2019. Motion-Aware Feature for Improved Video Anomaly Detection. arXiv preprint arXiv:1907.10211(2019).

Cited By

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  • (2022)Evaluation of the effectiveness of a crowdsourcing-based crime detection systemIEICE Communications Express10.1587/comex.2022XBL009911:9(607-611)Online publication date: 1-Sep-2022

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Published In

cover image ACM Conferences
SIGGRAPH '20: ACM SIGGRAPH 2020 Posters
August 2020
118 pages
ISBN:9781450379731
DOI:10.1145/3388770
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2020

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Author Tags

  1. anomaly detection
  2. motion-attentive network
  3. surveillance video

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  • Poster
  • Research
  • Refereed limited

Funding Sources

  • Institute for Information & Communications Technology Promotion, MSIP

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SIGGRAPH '20
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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Cited By

View all
  • (2022)Evaluation of the effectiveness of a crowdsourcing-based crime detection systemIEICE Communications Express10.1587/comex.2022XBL009911:9(607-611)Online publication date: 1-Sep-2022

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