Flame Detection Using Generic Color Model and Improved Block-Based PCA in Active Infrared Camera

In this paper, we proposed an all-weather flame detection algorithm which could make full use of active infrared cameras presently installed in many public places for surveillance purposes. Firstly, according to the different spectral imaging results in day and night, we propose a video type classification algorithm (VTCA) via imaging clues. VTCA could help us select different flame visual features in color image and infrared image. Secondly, we use a generic YCbCr-color-space-based chrominance model to extract regions of interest (ROI) of flame. Thirdly, two flame dynamic features are used to verify the candidate ROIs, which are common flame flicker feature and an improved block-based PCA in consecutive frames. The experimental results show that the proposed flame detection model has been successfully applied to various situations, including day and night, indoor and outdoor on our test video datasets, and it gives a better performance compared with other state-of-the-art methods.