TY  - JOUR
PY  - 2018//
TI  - A video based fire smoke detection using robust AdaBoost
JO  - Sensors (Basel)
A1  - Wu, Xuehui
A1  - Lu, Xiaobo
A1  - Leung, Henry
SP  - s18113780
EP  - s18113780
VL  - 18
IS  - 11
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s18113780
ID  - ref1
ER  -