Open AccessReal‐time image smoke detection using staircase searching‐based dual threshold AdaBoost and dynamic analysis
Author(s) -
Yuan Feiniu,
Fang Zhijun,
Wu Shiqian,
Yang Yong,
Fang Yuming
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.1032
Subject(s) - adaboost , artificial intelligence , computer science , robustness (evolution) , pattern recognition (psychology) , smoke , constant false alarm rate , computer vision , false alarm , rgb color model , support vector machine , engineering , biochemistry , waste management , chemistry , gene
It is very challenging to accurately detect smoke from images because of large variances of smoke colour, textures, shapes and occlusions. To improve performance, the authors combine dual threshold AdaBoost with staircase searching technique to propose and implement an image smoke detection method. First, extended Haar‐like features and statistical features are efficiently extracted from integral images from both intensity and saturation components of RGB images. Then, a dual threshold AdaBoost algorithm with a staircase searching technique is proposed to classify the features of smoke for smoke detection. The staircase searching technique aims at keeping consistency of training and classifying as far as possible. Finally, dynamic analysis is proposed to further validate the existence of smoke. Experimental results demonstrate that the proposed system has a good robustness in terms of early smoke detection and low false alarm rate, and it can detect smoke from videos with size of 320 × 240 in real time.