Video-based Fire Smoke Detection Using Temporal-spatial Saliency Features

Abstract

In this paper, we propose a video based spatial-temporal convolutional neural network for fire smoke recognition. The model concatenates the appearance features and the motion features followed by a convolution layer to implement spatial-temporal feature fusion. To reduce the influence of background of no-smoke, we use an attention module to capture salience features from the input image. Experiments on the self-created dataset show that the presented method is valid, which achieves a detection rate of 97.5% and accuracy rate of 96.8%.