Object-size invariant anomaly detection in video-surveillance

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  • Additional Information
    • Publication Information:
      IEEE
    • Publication Date:
      2017
    • Abstract:
      Nowadays, there is a growing demand for automated video-based surveillance systems due to increase security concerns. Anomaly detection is a popular application in this area where anomalous events of interest are defined as observed behavior that stands out from its context in space and time. In this paper, we present an approach for the detection of anomalous motion based on the extraction of object-size features that is independent of object size and video resolution. The proposed approach relies on a variable spatial window based on object size that has shown robustness in scenarios that present motion of objects of different sizes. We propose a system composed of four building blocks: background subtraction, feature extraction, event modeling and outlier detection. The proposed approach is evaluated on publicly available datasets which contain instances of abandoned objects of different sizes (considered as anomalies). The experiments carried out demonstrate that our approach outperforms the related state-of-the-art in the selected datasets. The proposal can identify anomalies associated to objects with different sizes and motion without increasing the number of false positives.
    • Contents Note:
      Conference Acronym: ICCST
    • Author Affiliations:
      Video Processing and Understanding Lab, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain
    • ISBN:
      978-1-5386-1585-0
    • ISSN:
      2153-0742
    • Relation:
      2017 International Carnahan Conference on Security Technology (ICCST)
    • Accession Number:
      10.1109/CCST.2017.8167826
    • Rights:
      Copyright 2017, IEEE
    • AMSID:
      8167826
    • Conference Acronym:
      ICCST
    • Date of Current Version:
      2017
    • Document Subtype:
      IEEE Conference
    • Notes:
      Conference Location: Madrid, Spain

      Conference Start Date: 23 Oct. 2017

      Conference End Date: 26 Oct. 2017
    • Accession Number:
      edseee.8167826
  • Citations
    • ABNT:
      SANMIGUEL, J. C.; MARTINEZ, J. M.; CARO-CAMPOS, L. Object-size invariant anomaly detection in video-surveillance. 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference on, [s. l.], p. 1–6, 2017. DOI 10.1109/CCST.2017.8167826. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.8167826. Acesso em: 28 set. 2020.
    • AMA:
      SanMiguel JC, Martinez JM, Caro-Campos L. Object-size invariant anomaly detection in video-surveillance. 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference on. October 2017:1-6. doi:10.1109/CCST.2017.8167826
    • APA:
      SanMiguel, J. C., Martinez, J. M., & Caro-Campos, L. (2017). Object-size invariant anomaly detection in video-surveillance. 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference On, 1–6. https://doi.org/10.1109/CCST.2017.8167826
    • Chicago/Turabian: Author-Date:
      SanMiguel, Juan C., Jose M. Martinez, and Luis Caro-Campos. 2017. “Object-Size Invariant Anomaly Detection in Video-Surveillance.” 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference On, October, 1–6. doi:10.1109/CCST.2017.8167826.
    • Harvard:
      SanMiguel, J. C., Martinez, J. M. and Caro-Campos, L. (2017) ‘Object-size invariant anomaly detection in video-surveillance’, 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference on, pp. 1–6. doi: 10.1109/CCST.2017.8167826.
    • Harvard: Australian:
      SanMiguel, JC, Martinez, JM & Caro-Campos, L 2017, ‘Object-size invariant anomaly detection in video-surveillance’, 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference on, pp. 1–6, viewed 28 September 2020, .
    • MLA:
      SanMiguel, Juan C., et al. “Object-Size Invariant Anomaly Detection in Video-Surveillance.” 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference On, Oct. 2017, pp. 1–6. EBSCOhost, doi:10.1109/CCST.2017.8167826.
    • Chicago/Turabian: Humanities:
      SanMiguel, Juan C., Jose M. Martinez, and Luis Caro-Campos. “Object-Size Invariant Anomaly Detection in Video-Surveillance.” 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference On, October 1, 2017, 1–6. doi:10.1109/CCST.2017.8167826.
    • Vancouver/ICMJE:
      SanMiguel JC, Martinez JM, Caro-Campos L. Object-size invariant anomaly detection in video-surveillance. 2017 International Carnahan Conference on Security Technology (ICCST), Security Technology (ICCST), 2017 International Carnahan Conference on [Internet]. 2017 Oct 1 [cited 2020 Sep 28];1–6. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.8167826