A motion and lightness saliency approach for forest smoke segmentation and detection

Abstract

Most video-based detection systems rely on extracting visual features directly from divided original frames or detected motion regions achieved using conventional background subtraction. However, these approaches are not effective for smoke detection because conventional motion detection methods are sensitive to non-salient motion regions like waves, shaking tree leaves, camera jitter an so on. This tiny motion regions can be easily misclassified as smoke. Second, in the case of light smoke the background is visible with or without motion detection, such that it will deteriorate the feature quality. These regions usually occur far from smoke centers and present unstable and non-salient characteristics. This paper proposes an approach to separate the smoke region based on motion and lightness saliency detection. A low-rank and structured sparse decomposition method is used to extract the foreground regions. Saliency of smoke is then computed for further separation. These aforementioned measures ensure a reliable smoke component extraction. We propose a saliency measurement for group-sparse robust orthonormal subspace and learning (ROSL) in virtue of adaptive parameters. Experiments on challenging data sets demonstrate that the proposed method can work well on a wide range of smoke videos and give better smoke detection results.