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Multi-property Tensor-Based Learning for Abnormal Event Detection

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Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13598))

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Abstract

In this paper, we propose a novel abnormal event detection scheme for video surveillance systems using an unsupervised learning process. Our contribution includes intra and inter property feature encoding to reduce information redundancy within (intra) and across (inter) image features. Intra property encoding is carried out using convolutional auto-encoders. Inter-property encoding is performed using an unsupervised tensor-based learning mode to handle the dimensionality issue arising in cases when different properties are inter-related together. Comprehensive experiments are performed on two benchmarks: Avenue, and ShanghaiTech.

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Authors and Affiliations

  1. National Technical University of Athens, 15773, Athens, Greece

    Nikolaos Bakalos, Nikolaos Doulamis & Anastasios Doulamis

  2. Institute of Digital Games, University of Malta, Msida, 2080, MSD, Malta

    Konstantinos Makantasis

Authors
  1. Nikolaos Bakalos
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  2. Nikolaos Doulamis
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  3. Anastasios Doulamis
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  4. Konstantinos Makantasis
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Corresponding author

Correspondence to Nikolaos Bakalos .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

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Bakalos, N., Doulamis, N., Doulamis, A., Makantasis, K. (2022). Multi-property Tensor-Based Learning for Abnormal Event Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_25

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  • DOI: https://doi.org/10.1007/978-3-031-20713-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20712-9

  • Online ISBN: 978-3-031-20713-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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