Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos

Fire is one of the most devastating hazards that can cause serious damage to human life, health and property. As smoke is often an initial sign of fire, smoke detection using surveillance cameras is key to providing early alarm in open space environments. In this paper, we propose a new feature extraction method that combines local binary patterns with co-occurrence of texture features in RGB color space to characterize the diverse manifestations of smoke. The proposed RGB color based Local Binary Co-occurrence Patterns (RGB_LBCoP) extracts smoke features from candidate smoke regions which are extracted by Fuzzy C-Means (FCM) algorithm. Subsequently, Support Vector Machine (SVM) is used for training and classification based on these features. The major benefit of the proposed feature descriptor is the ability to incorporate local and global texture properties of smoke along with color information. This property enables the detection of smoke in complex environments and provides insensitivity to illumination changes. For validation, performance of the proposed method is compared with other LBP variants and Gray-level co-occurrence matrix (GLCM). Experimental analysis on publicly available smoke video datasets demonstrates that the proposed algorithm outperforms the other methods by achieving an average True Positive Rate (TPR) of 92.02%.