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Video Anomaly Detection for Smart Surveillance

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Computer Vision

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Definition

Anomalies in videos are broadly defined as events or activities that are unusual and signify irregular behavior. The goal of anomaly detection is to temporally or spatially localize the anomaly events in video sequences. Temporal localization (i.e., indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection. Spatial localization, which is more challenging, means to identify the pixels within each anomaly frame that correspond to the anomaly event. This setting is usually referred to as pixel-level detection.

Background

In modern intelligent video surveillance systems, automatic anomaly detection through computer vision analytics plays a pivotal role which not only significantly increases monitoring efficiency but also reduces the burden on live monitoring. Video anomaly detection has been studied for a long time, while this problem is far from being solved...

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Notes

  1. 1.

    https://www.youtube.com/

  2. 2.

    https://www.aicitychallenge.org/

  3. 3.

    https://www.aicitychallenge.org/

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Acknowledgements

This work was supported by UNCC Faculty Research Grant 111206, and in part by NSF CNS-1910844.

Author information

Authors and Affiliations

  1. University of North Carolina at Charlotte, Charlotte, NC, USA

    Sijie Zhu & Chen Chen

  2. Information Technology University, Lahore, Pakistan

    Waqas Sultani

Authors
  1. Sijie Zhu
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  2. Chen Chen
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  3. Waqas Sultani
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Corresponding author

Correspondence to Chen Chen .

Section Editor information

  1. No affiliation provided

    Mubarak Shah

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Zhu, S., Chen, C., Sultani, W. (2020). Video Anomaly Detection for Smart Surveillance. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_845-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_845-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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