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Review
. 2022;11(2):85-110.
doi: 10.1007/s13735-022-00227-8. Epub 2022 Mar 29.

Anomaly detection using edge computing in video surveillance system: review

Devashree R Patrikar  1 , Mayur Rajaram Parate  1
Affiliations
Review

Anomaly detection using edge computing in video surveillance system: review

Devashree R Patrikar et al. Int J Multimed Inf Retr. 2022.

Abstract

The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

Keywords: Anomaly detection; Edge computing; Machine learning; Video surveillance.

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Conflict of interest statement

Conflicts of interestBoth the authors have checked the manuscript and have agreed to the submission in International Journal of Multimedia Information Retrieval. There is no conflict of interest between the authors.

Figures

Fig. 1
Fig. 1
Anomaly detection in video surveillance scenes. a A truck moving on the footpath (UCSD Dataset); b Pedestrian walking on the lawn (UCSD Dataset); c A person throwing an object (Avenue); d a person carrying a suspicious bag (Avenue); e Incorrect parking of vehicle (MDVD); f people fighting (MDVD); g a person catching a bag (ShanghaiTech); h vehicles moving on the footpath (ShanghaiTech)
Fig. 2
Fig. 2
General block diagram of anomaly detection
Fig. 3
Fig. 3
Timeline for evolution of anomaly detection techniques
Fig. 4
Fig. 4
Correlation of surveillance, surveillance targets, and associated anomalies
Fig. 5
Fig. 5
Anomaly classification
Fig. 6
Fig. 6
Architectural overview of edge computing
See this image and copyright information in PMC

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