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Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

Anomaly detection in surveillance videos is a challenging problem in computer vision community. In this paper, a novel unsupervised learning framework is proposed to detect and localize abnormal events in real-time manner. Typical methods mainly rely on extracting complex handcraft features and learning only a fitting model for prediction. In contrast, normal events are represented using simple spatio-temporal volume (STV) in our method, then adaptive multiple auto-encoders (AMAE) are constructed to handle the inter-class variation in normal events. When testing on an unknown frame, reconstruction errors of multiple auto-encoders are utilized for prediction. Experiments are performed on UCSD Ped2 and UMN datasets. Experimental results show that our method is effective to detect and localize abnormal events at a speed of 70 fps.

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Acknowledgment

This work was supported by special fund of Chinese Academy of Sciences, with grant number XDA060112030.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, China

    Tianlong Bao, Chunhui Ding, Saleem Karmoshi & Ming Zhu

Authors
  1. Tianlong Bao
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  2. Chunhui Ding
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  3. Saleem Karmoshi
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  4. Ming Zhu
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Corresponding author

Correspondence to Tianlong Bao .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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Bao, T., Ding, C., Karmoshi, S., Zhu, M. (2016). Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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