Fire detection system using random forest classification for image sequences of complex background

Onecue Kim,Dong-Joong Kang

doi:10.1117/1.oe.52.6.067202

Abstract

We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

Highlights

Conventional fire detection systems were designed to detect smoke, heat, and radiant energy from a fire using infrared, optical, and ion sensors.[1,2,3]. These methods have problems, namely, it is impossible to tell whether a fire is occurring until smoke or flame spreads to the detection range of sensors and detection takes a long time because a fire alarm is only issued after the flame’s influence exceeds a reference temperature or a set value
A fire detection system using image processing does not require any additional costs, as it uses surveillance cameras already installed in public places, roads, and tunnels
Fire features were modeled by color, motion, and crossing features

Summary

Introduction

Conventional fire detection systems were designed to detect smoke, heat, and radiant energy from a fire using infrared, optical, and ion sensors.[1,2,3] these methods have problems, namely, it is impossible to tell whether a fire is occurring until smoke or flame spreads to the detection range of sensors and detection takes a long time because a fire alarm is only issued after the flame’s influence exceeds a reference temperature or a set value. Fire detection algorithms used rule-based color models.[4,5,6] They classify the image pixels as fire or not fire by using the RGB, hue, saturation and intensity, and YCbCr color models. Töreyin et al.[8] used temporal and spatial wavelet analysis to separate the fire regions from sequential images They presented good experimental results, this approach is impractical in various environments due to many heuristic thresholds. All modules, including image features extraction and fire classification, are realized to test fire images of different conditions and verified to be robust in complex environments

Embedded Camera System

Learning RF and Classifying Fire Features

Decision of a Fire

Experiments

Conclusions