Smoke detection method using Local Binary Patterns and AdaBoost

To realize quick and robust fire detection with image information of real scenes, smoke is a key feature information in detection methods. Since smoke does not keep stationary shape, it is difficult apply ordinal image processing techniques such as the edge or contour detection directly. Image information of smoke is also affected from its environmental conditions such as illumination changes and background objects. In this study, we adopt Local Binary Patterns (LBP), which is defined as a simple texture operator computed using the center pixel value and its neighborhood pixel values. From its definition, LBP is a robust image descriptor against the illumination change. Additionally, we also adopt AdaBoost, one of the widely used learning methods, to improve the accuracy of detection results. The adaptive detection for real-scene situations is realized by AdaBoost. Results using with real scene data show that the presented method can provide accurate results against the various conditions of real world situations.