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ABSTRACT
The objective of this study is to create an enhanced fire detection system that can analyse offline recordings and generate real-time alerts to reduce the risks connected with fire occurrences. In an era when video surveillance is widely used for safety and security, detecting and responding to fire dangers in recorded footage is critical. This research aims to employ machine learning and computer vision techniques to create a robust and efficient fire detection algorithm that can work in offline video contexts. The primary goal is to improve overall safety and emergency response processes by sending timely alerts to stakeholders, reducing property damage, and injuries, and potentially saving lives.
