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Learning Motion Regularity for Temporal Video Segmentation and Anomaly Detection

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Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

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Abstract

Detecting anomalous events in a long video sequence is a challenging task due to the subjective definition of “anomalous” as well as the duration of such events. Anomalous events are usually short-lived and occur rarely. We propose a semi-supervised solution to detect such events. Our method is able to capture the video segment where the anomaly happens via the analyses of the interaction between the spatially co-located interest points. The evolution of their motion characteristics is modeled and abrupt changes are used to temporally segment the videos. Spatiotemporal and motion features are then extracted to model standard events and identify the anomalous segments using a one-class classifier. Quantitative and qualitative experiments on publicly available anomaly detection dataset capturing real-world scenarios show that the proposed method outperforms state-of-the-art approaches.

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Acknowledgement

This work was performed in part through the financial assistance award, Multitiered Video Analytics for Abnormality Detection and Alerting to Improve Response Time for First Responder Communications and Operations (Grant No. 60NANB17D178), from U.S. Department of Commerce, National Institute of Standards and Technology.

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Authors and Affiliations

  1. University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA

    Fatima Zohra Daha & Shishir K. Shah

Authors
  1. Fatima Zohra Daha
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  2. Shishir K. Shah
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Corresponding author

Correspondence to Shishir K. Shah .

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Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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Daha, F.Z., Shah, S.K. (2020). Learning Motion Regularity for Temporal Video Segmentation and Anomaly Detection. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-41404-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41403-0

  • Online ISBN: 978-3-030-41404-7

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