Fire Alarm Using Multi-rules Detection and Texture Features Classification in Video Surveillance

This paper describes an efficient fire detection approach using color and texture information. Our approach consists of Fire Pixel Based Multi-rules Detection and Fire Texture Based Classification. We segment the fire candidate region in HIS and RGB color space using multi rules based on statistic color model, and then employ LBP to extract the fire texture features from those candidate regions. Finally, a SVM classifier is trained to determine the real fire region by these LBP features. To improve the predicting precision, we conduct cross validation to select the best parameters for SVM training model. The performance of the proposed approach is tested on various types of fire scene videos. Testing results show that it is effective on fire alarm and can give an early detection on video surveillance.