Fire Recognition Based on Sensor node and Feature of Video Smoke

Gaussian mixed model, LK optical flow method and background subtraction from foreground method are used to extract the fire and smoke region in foreground of video image. Multi feature of fire characteristics are used to extract the information. Colour feature of suspected region are extracted according to the colour model RGB and HSI spaces. Background blur feature is extracted using two dimensional discrete wavelet transform. If smoke appears in scene, the contour edge of the background would become blurry. The motion direction feature is extracted using LK optical flow method and gaussion mixed model. The DHT 11 digital temperature - humidity sensor in sensor node is used to extract temperature and humidity values for measurement and TIMSP430 microcontroller for processing the information. The video node and sensor node extracted information are combined to detect the possibility of fire in the area during worst season conditions. By this method, the accuracy of fire and smoke detection is improved even in the worst environmental condition such as rainy weather. From the simulated and experimental results, the proposed method improves the accuracy and detection rate. Combination of sensor output and video output give excellent value in finding smoke or fire from videos. They reduces false detection rate of detecting smoke from non-smoke videos. It can be used in outdoor large environment.