Random Forest Feature Selection and Back Propagation Neural Network to Detect Fire Using Video

Jin-Xing Liang,Jian-Fu Zhao,Bao-Jun Shi,Ning Sun,Mu Zhou

doi:10.1155/2022/5160050

Abstract

As the most common serious disaster, fire may cause a lot of damages. Early detection and treatment of fires are of great significance to ensure public safety and to reduce losses caused by fires. However, traditional fire detectors are facing some focus issues such as low sensitivity and limited detection scenes. To overcome these problems, a video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed. The improved flame color model in RGB and HSI space and the visual background extractor (ViBe) in moving target detection algorithm are used to segment the suspected flame regions. Then, multidimensional features of flames are extracted from the suspected regions, and these extracted features are combined and selected according to the RF feature importance analysis. Finally, a BP neural network model is constructed for multifeature fusion and fire recognition. The test results on several experimental video sets show that the proposed method can effectively avoid feature interference and has an excellent recognition effect on fires in a variety of scenarios. The proposed method is applicable for fire recognition applied in video surveillance and detection robots.

Highlights

With the continuous improvement of social development, the possibility of fires with large losses is increasing
An effective video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed
The improved color model and visual background extractor (ViBe) algorithm are used to segment the suspected flame regions, and the RF importance analysis method is used for the feature combination and selection

Summary

Introduction

With the continuous improvement of social development, the possibility of fires with large losses is increasing. Chen et al [2] proposed a flame recognition rule in RGB and HSI color space by analyzing the chromaticity and disorder of flames. Prema et al [5] extracted texture features based on wavelet decomposition and used an extreme learning classifier to classify images. Their method can effectively eliminate red interference. Jamali et al [7] introduced texture features based on color features to detect fires. They combined different features to improve the accuracy of the fire detection system. After a certain period of time t, the probability Pðt, t + dtÞ that a certain sample in the sample set is still retained can be defined as follows:

Methods

Results

Conclusion