Smoke detection in video using dissipation function and support vector machine

Abstract

A method for smoke detection in video is proposed. The camera monitoring the scene is assumed to be stationary. With the atmospheric scattering model, dissipation function is reflected the transmissivity between the background objects in the scene and the camera. Dark channel prior and fast bilateral filter are used for estimating dissipation function which is only the function of the depth of field. Based on dissipation function, visual background extractor (ViBe) can be used for detecting smoke as a result of smoke’s motion characteristics as well as detecting other moving targets. Later on, various characteristics of smoke are extracted. Color feature and high-frequency energy based on wavelet transformation is selected to constitute the final recognition vectors, and Support vector machine (SVM) is used as a classification model. The final experimental results show that the accuracy rate of this method for smoke detection can reach 90.1%.