Anomaly Detection using Edge Computing in Video Surveillance System: Review

TL;DR

This paper reviews various methodologies for anomaly detection in video surveillance, emphasizing edge computing approaches, categorizing techniques, and discussing challenges and opportunities in deploying these systems in smart city infrastructures.

Contribution

It provides a systematic categorization of anomaly detection methods with a focus on edge computing, highlighting recent developments and challenges in smart city surveillance systems.

Findings

Edge-based anomaly detection approaches are gaining importance.

Context-aware anomaly detection improves accuracy.

Challenges include computational constraints and data privacy.

Abstract

The current concept of Smart Cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and give 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 surveyed various methodologies developed to detect anomalies in intelligent video surveillance. Firstly, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies developed for ease of understanding. Considering the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is considered a time-critical application of computer vision, our emphasis is on anomaly detection using edge devices and approaches explicitly designed for them. Further, we discuss the challenges and opportunities involved in anomaly detection at the edge.