Smoke Detection Based on a Semi-supervised Clustering Model

Abstract

Video-based smoke detection is regarded as an effective way for fire detection in open spaces. In this paper, a classification model based on a semi-supervised clustering method is introduced to improve the performance of smoke detection. In our model, we present a novel method to automatically determine the number of clusters K. Considering the randomness of the initial centers in K-means++, a voting strategy is proposed to maintain a stable clustering performance. Besides, the scene-related information is added to our clustering data to obtain a self-adaptive model. Finally, the experimental results show that our classification model outperforms other state-of-the-art methods and has great improvement in terms of generalization (i.e. can adapt to the unknown scenes).KeywordsSmoke detectionVoting strategySelf-adaptiveSemi- supervised