Spatio-temporal analysis in smoke detection

Smoke detection in video surveillance images has been studied for years. However, given an image in open or large spaces with typical smoke and the disturbance of commonly moving objects such as pedestrians or vehicles, robust and efficient smoke detection is still a challenging problem. In this paper, we present a novel and reliable framework for automatic smoke detection. It exploits three features: edge blurring, the gradual change of energy and the gradual change of chromatic configuration. In order to gain proper generalization ability with respect to sparse training samples, the three features are combined using a support vector machine based classifier. This system has been run more than 6 hours in various conditions to verify the reliability of fire safety in the real world.