Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Early fire detection method in video for vessels
Journal of Systems and Software
Moving object detection in the H.264/AVC compressed domain for video surveillance applications
Journal of Visual Communication and Image Representation
Effective distributed service architecture for ubiquitous video surveillance
Information Systems Frontiers
Fire flame detection in video sequences using multi-stage pattern recognition techniques
Engineering Applications of Artificial Intelligence
Digital Signal Processing
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In this paper, we propose a real-time fire-detector that combines foreground object information with color pixel statistics of fire. Simple adaptive background model of the scene is generated by using three Gaussian distributions, where each distribution corresponds to the pixel statistics in the respective color channel. The foreground information is extracted by using adaptive background subtraction algorithm, and then verified by the statistical fire color model to determine whether the detected foreground object is a fire candidate or not. A generic fire color model is constructed by statistical analysis of the sample images containing fire pixels. The first contribution of the paper is the application of real-time adaptive background subtraction method that aids the segmentation of the fire candidate pixels from the background. The second contribution is the use of a generic statistical model for refined fire-pixel classification. The two processes are combined to form the fire detection system and applied for the detection of fire in the consecutive frames of video sequences. The frame-processing rate of the detector is about 40 fps with image size of 176x144 pixels, and the algorithm's correct detection rate is 98.89%.