The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier

Abstract

For existed problems on fire detection fields, the traditional recognition methods on fire usually based on sensor’s signals are easily affected by the external environment elements. Meanwhile, most of the current methods based on feature extraction of fire image are less discriminative to different scene and fire type, and have lower recognition precision if the fire scene and type change. To overcome the drawback on fire recognition, the new fast recognition method for fire image has proposed by introducing color space information into Scale Invariant Feature Transform (SIFT) algorithm. Firstly, the feature descriptors of fire are extracted by SIFT algorithm from the fire images which are obtained from internet databases. Secondly, the local noisy feature points are filtered by introducing the feature information of fire color space. Thirdly, the feature descriptors are transformed into feature vectors, and then Incremental Vector Support Vector Machine classifier is utilized to establish the fast fire recognition model. The experiments are conducted on real-life fire image from internet. The experimental results had shown that for different fire scenes and types, the proposed algorithm has outperformed Kim’s method, Dimitropoulos’s method and Sumei’s method in terms of recognition accuracy and algorithm’s running speed. The proposed algorithm has better application prospects than Kim’s method, Dimitropoulos’s method and Sumei’s method.