Context-Aware Detection of Mixed Critical Events using Video Classification

TL;DR

This paper presents a versatile video classification system for detecting mixed-critical events in smart cities, capable of understanding context to accurately identify incidents like fires and traffic accidents.

Contribution

It introduces a novel adaptable detection system that addresses the challenges of contextual understanding in mixed-critical event detection across various scenarios.

Findings

Effective detection of fire and traffic incidents demonstrated

System adapts to diverse application contexts

Advances automated surveillance in smart city environments

Abstract

Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptable systems that can interpret context to trigger appropriate responses. This paper addresses these challenges by proposing a versatile detection system for smart city applications, offering a solution tested across traffic and fire detection scenarios. Our contributions include an analysis of detection requirements and the development of a system adaptable to diverse applications, advancing automated surveillance for smart cities.