Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence

This paper presents a novel algorithm for detection certain types of emergencies relating to fire, smoke and explosions by processing the data recorded from the camera monitoring, based on cascaded approach. First, the combination of Adaboost and Local binary pattern (LBP) are using for getting Region of Interest (ROI) and reducing time complexity. Next, to alleviate common problems of vulnerable such as false positive, we propose to use Convolutional Neural Network (CNN). The final experimental results showed that the accuracy rate of this method for emergencies detection could reach 95.2%.