A Saliency-Based Method for Early Smoke Detection in Video Sequences

Abstract

Video-based smoke detection requires suspected smoke regions to be segmented from the complex background in the initial stage of detection. This segmentation is also important to the subsequent processes of detection. This paper proposes a novel method of segmenting a smoke region in smoke pixel classification based on saliency detection. A salient smoke detection model based on color and motion features is used. First, smoke regions are identified by enhancing the smoke color nonlinearly. The enhanced map and motion map are then used to measure saliency. Finally, the motion energy and saliency map are used to estimate the suspected smoke regions. The estimation result is regarded as our final smoke pixel segmentation result. The performance of the proposed algorithm is verified on a set of videos containing smoke. In the experiments, the method achieves average smoke segmentation precision of 93.0%, and the precision is as high as 99.0% for forest fires. The results are compared with those of three other methods used in the literature, revealing the proposed method to have both a better segmentation result and better precision. We also present encouraging results of smoke segmentation in video sequences obtained using the proposed saliency detection method. Furthermore, the proposed smoke segmentation method can be used for real-time fire detection in color video sequences.