A Video Based Fire Smoke Detection Using Robust AdaBoost.

Xuehui Wu,Henry Leung,Xiaobo Lu

doi:10.3390/s18113780

Xuehui Wu, Henry Leung + Show 1 more

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https://doi.org/10.3390/s18113780

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Abstract

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

Highlights

Detection fire smoke at the early stage has drawn a lot of attentions recently due to its importance to social security and economic development
When we focus on the Zm with error less than 0.5, it is clear that the proposed robust AdaBoost is consistent with the boosting function, it is appropriate for the entire domain of definition [0,1], which is more robust than conventional AdaBoost
We evaluate the performance of Robust AdaBoost (RAB) and the improved Robust

Summary

Introduction

Detection fire smoke at the early stage has drawn a lot of attentions recently due to its importance to social security and economic development. Conventional point fire smoke detector sensors are effective for indoor applications, but they have difficulties to detect smoke in large outdoor areas, it is because point fire smoke detector typically detect the presence of certain particles generated by smoke and fire by ionization [1], photometry [2], or smoke temperature [3,4]. They require a close proximity to fire and smoke, which are not effective for open spaces. Video based fire smoke detection using cameras is of great interest in large and open spaces [5].

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