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Insights of Deep Learning-Based Video Anomaly Detection Approaches

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 171))

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Abstract

Deep learning is a powerful computing strategy that has changed the landscape of computer vision. It has been used to tackle complicated cognitive tasks such as detecting abnormalities in videos. Anomalies in the video are events or objects in the footage that don’t fit the typical, learned patterns. Using deep learning, it is possible to automatically and in real-time identify unusual actions and objects like fights, riots, traffic rule violations, abrupt rushes, and the presence of weapons in restricted areas or abandoned luggage. Despite the challenges posed by video anomaly detection, this review offers a comprehensive assessment of published deep learning algorithms for the task. Future research can build on this work by understanding the existing methods to create more effective solutions. First, the challenges of video anomaly identification are discussed as the benefits of deep learning in anomaly detection. Furthermore, several types of abnormalities were explored, followed by diverse methodologies for anomaly identification. Furthermore, significant aspects of anomaly detection using deep learning, including learning approaches, were presented. Finally, numerous datasets used in anomaly detection were examined, followed by a discussion of deep learning-based algorithms for spotting video anomalies.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), Charotar University of Science and Technology (CHARUSAT), Changa, 388421, India

    Dipak Ramoliya

  2. Department of Computer Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering, Charotar University of Science and Technology (CHARUSAT), Changa, Anand, 388421, India

    Amit Ganatra

  3. Computer Science and Engineering Department, Faculty of Engineering and Technology, Parul University (PU), Waghodia, Vadodara, 391760, India

    Amit Ganatra

Authors
  1. Dipak Ramoliya
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  2. Amit Ganatra
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Corresponding author

Correspondence to Dipak Ramoliya .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India

    G. Rajakumar

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. ISEG—University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

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Ramoliya, D., Ganatra, A. (2023). Insights of Deep Learning-Based Video Anomaly Detection Approaches. In: Rajakumar, G., Du, KL., Rocha, Á. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-99-1767-9_48

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  • DOI: https://doi.org/10.1007/978-981-99-1767-9_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1766-2

  • Online ISBN: 978-981-99-1767-9

  • eBook Packages: EngineeringEngineering (R0)

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