[1]YAN Yunyang,CHEN Chuixiong,LIU Yian,et al.Fire detection based on dynamic texture featuresunder a dimension-weighted mode[J].CAAI Transactions on Intelligent Systems,2017,12(4):548-555.[doi:10.11992/tis.201607021]
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Fire detection based on dynamic texture featuresunder a dimension-weighted mode

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 4 Page number: 548-555 Column: 学术论文—机器感知与模式识别 Public date: 2017-08-25
Title:
Fire detection based on dynamic texture featuresunder a dimension-weighted mode
Author(s):
YAN Yunyang1; 2;  CHEN Chuixiong1; 2;  LIU Yi’an2;  GAO Shangbing1
1. Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
static texture; dynamic texture; orthogonal feature; weighted feature; support vector machine; flame detection; feature extraction; local binary pattern
CLC:
TP391
DOI:
10.11992/tis.201607021
Abstract:
In fire detection modeling, a local ternary pattern is generally used to extract the static and dynamic textures of the suspected flame. But it is difficult to distinguish the flame from other uniform texture interferences when a local ternary pattern is used to describe the static texture features. The efficiency is low when the KNN (k-Nearest Neighbor) algorithm is used for classification. Aimed at solving these problems, a novel method is proposed here, whereby an LMP-TOP (local mixed pattern-three orthogonal planes) method is used to depict the static and dynamic textures of a suspected flame area. A dimension-weighted support vector machine was used for the classification. Applying LMP-TOP, an eight neighborhood uniform local binary pattern (LBPu2) was used to denote the static texture features of the flame on the 1st-dimension plane XY, and a local ternary pattern was used to describe the change in flame information on the 2nd-and 3rd -dimension planes, XT and YT respectively, by fusing with information in the time dimension. The static and dynamic characteristics of the flame were therefore integrated. The dimension weight was assigned according to the individual recognition accuracy. Then, a support vector machine with dimension weighting was used for classification. Experimental results show that the accuracy of flame identification and the detection efficiency are better with the proposed method than with corresponding algorithms such as Sthevanie.
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