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).
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He, H., Peng, L., Yang, D., Chen, X. (2014). Smoke Detection Based on a Semi-supervised Clustering Model. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_27
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DOI: https://doi.org/10.1007/978-3-319-04117-9_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04116-2
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