Citation

Wu X, Lu X, Leung H. Sensors (Basel) 2018; 18(11): s18113780.

Affiliation

Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr N.W., Calgary, AB T2N 1N4, Canada. leungh@ucalgary.ca.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18113780

PMID

30400645

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.

Language: en

Keywords

classifier; feature extraction; fire smoke detection; robust AdaBoost; video based