Analysis of shape features of flame and interference image in video fire detection

In this paper, the shape features of different material of flame and interference image in video fire detection were analyzed and compared. The area, perimeter, rotundity, solidity, extent, sharp corners, similarity and centroid displacement are selected as the candidate features. After analyzing and excluding area and perimeter as these candidate features, weak classifiers of six other shape features are established, and then calculate weights of each weak classifier using AdaBoost algorithm, and then sort according to the weights of weak classifiers, and then proposed the preference of choosing shape features. In this paper, shape features selection preference is introduced, which can be used as a reference for video fire detection choosing shape features.

[1]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[2]  Chao-Ho Chen,et al.  An early fire-detection method based on image processing , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[3]  Du Xiao-xiao.  Application of image recognition technology in fire detection system of ship , 2007 .

[4]  Li Wen-hui,et al.  High-precision video flame detection algorithm based on multi-feature fusion , 2010 .

[5]  Dengyi Zhang,et al.  SVM based forest fire detection using static and dynamic features , 2011, Comput. Sci. Inf. Syst..

[6]  Ma Xian-min.  Recognition of fire flame based on image futures , 2012 .

[7]  Jialin Wang,et al.  A SVM approach for vessel fire detection based on image processing , 2012, 2012 Proceedings of International Conference on Modelling, Identification and Control.

[8]  Ying Cao,et al.  Advance and Prospects of AdaBoost Algorithm , 2013, ACTA AUTOMATICA SINICA.

[9]  Wang Wen-ha.  Fire Detection Based on Connected Region and SVM Feature Fusion , 2014 .

[10]  Klaus Diepold,et al.  Comparison of intensity flickering features for video based flame detection algorithms , 2014 .

[11]  Nai-Kong Fong,et al.  Experimental study of video fire detection and its applications , 2014 .

[12]  Felix Kümmerlen,et al.  Image processing based deflagration detection using fuzzy logic classification , 2014 .

[13]  Chang-Tien Lu,et al.  Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model , 2016, J. Intell. Transp. Syst..

[14]  Hongmei Yan.  Study on Algorithm of Fire Detection Based on Fusion of Multiple Features , 2017 .