A Scalable and Robust Framework for Intelligent Real-time Video Surveillance

TL;DR

This paper introduces a scalable, fault-tolerant real-time video surveillance framework using Apache Storm and OpenCV, capable of efficient data processing and easy integration of new algorithms for large-scale monitoring.

Contribution

The authors develop an extensible, storage-efficient, and robust video surveillance system that leverages Storm topology for real-time processing and modular algorithm integration.

Findings

Framework is highly scalable and fault tolerant

Efficient storage by writing only important content

Supports addition of new algorithms without system disruption

Abstract

In this paper, we present an intelligent, reliable and storage-efficient video surveillance system using Apache Storm and OpenCV. As a Storm topology, we have added multiple information extraction modules that only write important content to the disk. Our topology is extensible, capable of adding novel algorithms as per the use case without affecting the existing ones, since all the processing is independent of each other. This framework is also highly scalable and fault tolerant, which makes it a best option for organisations that need to monitor a large network of surveillance cameras.