Fire Detection Using Video Images and Temporal Variations

Abstract

Fire detection is very crucial to the security and important to preserve the properties of citizens. On fire detection, various features such as extracted information from video and others have been used. The combination of various features can improve the accuracy of fire detection. Usually video images are an important resource for this task, and prior knowledge about colors and variations of fires can be used. Recently, deep neural network has shown the best performance in many task in computer visions. Thus, the use of deep neural network in fire detection has risen, but there were little works to use the temporally summarized information from the prior knowledge. To construct the deep neural network architecture reflecting this information and validate its performances, we gathered video clips and proposed the deep neural network using the temporal information from video clips is proposed. Analysis of real data showed that the proposed method improve the accuracy significantly. To summarize the temporal information we use the standard deviation of G-filter values of images along the time. By using this information, the more compact architecture can be constructed.