Saliency-Based Forest Fire Flame Recognition

Forest fires will greatly damage forest resources as well as constitute a threat to human life and property. In order to diminish the loss caused by forest fires, it is of significant importance to take some preventions and monitoring forest fires. Video surveillance is the commonly used means of forest fire monitoring, based on which using computer vision technology to realize automatic detection of forest fires has important practical significance.This paper employs saliency detection method to detect forest fire flames, and segment the salient object by Mean Shift algorithm, last extracting color feature and sharp angle feature of fire flames for recognition.When humans observe a scene, their attention is not equally distributed over the whole scene, instead, their attention will be quickly drawn to particular regions thus other parts get little attention. The particular regions that attract attention are referred to as salient regions and the phenomenon is Visual Saliency. When forest fire occurs, the fire regions have the property of saliency with regard to the surroundings. Then exploiting saliency method can quickly detect the corresponding regions. Our research and conclusions are as follows:(1)This paper gives an intensive study of some representative methods of saliency, including IT method that bases on human vision attention mechanism, SR method that bases on the spectral residual of Fourier transform, FT method that bases on frequency tuning, HC & RC methods that base on global color contrast. All of these methods are applied to detect forest fire flames and the results show that the FT method has a good performance in detecting forest fires.(2) Study and analyze the commonly used segmentation algorithms such as edge-detection based and threshold based methods. These kinds of methods are not satisfying when confront with complex scenes. Employ Mean Shift method, which is self-adaptive and doesn’t need any prior knowledge, for image segmentation. By clustering in feature space, Mean Shift method can achieve a good segmentation of forest fires.(3) Analyze the commonly used features of forest fire flames, and thus the color and sharp angle are selected as the criterions to distinguish forest fire flames. Experiments are carried out in RGB, HSI and YCbCr color spaces, and constraints for fire flame pixels are set in each color space, respectively. The results show that using the YCCr color space achieves the best result. Encode the boundary by chain code and give the thought that if there is a valid rise and fall in fairly short steps of chain code there is a sharp angle. The color and sharp angle can represent forest fire flame features.