Adaboost Classification Based Smoke Detection System Using Fully Connected Weighted HOG and Projection Vectors

Fire flame and smoke detection is challengeable task in the view scenes due to the variety shapes and colour to use the early alarm system. Because smoke is occurred in the early stage of the fire, it is very important to halt the fire’s progress. This article suggests a fully connected system which is used two features using Adaboost algorithm for constructing a strong classifier as linear combination. We first calculate the local histogram feature by gradient and bin, local binary pattern, and projection vectors for each cell. According to the histogram magnitude, this paper applied adapted weighting value to improve the recognition rate. To preserve the local region information which has edge intensity, this paper processed the normalization sequence. For the extracted features, this paper used the Adaboost algorithm which makes strong classification to classify the objects. Our experimental results show that proposed smoke detection system leads to higher detection accuracy than other system.