A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems

Detecting smoke during the initial stages is vital for preventing fire events. This study proposes a video-based approach for alarm systems that detects smoke based on temporal features extracted from optical smoke flow pattern analysis and spatial-temporal energy analysis. To do this, it considers various optical characteristics such as the diffusion, color, and semi-transparency of smoke. In the proposed model, smoke-colored pixels are identified via masking in the HSV color space and a temporal frame difference is applied. To extract the temporal feature vectors, we propose a new method that determines the optical flow of smoke by using distinguished texture information by applying a Gabor filter bank with preferred orientations. In addition, when applied to an image that has been temporal-differenced, the energy of the spatial frequencies is fed as another feature into the feature vector. Finally, these features are fed to a support vector machine (SVM) to discriminate our data more thoroughly and provide accurate detection of smoke. Experiments are carried out with benchmark datasets, which show that the proposed approach can work effectively without false alarms.