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Margarita, Favorskaya
Anna, Pyataeva
Aleksei, Popov
2018-02-07T07:31:19Z
2018-02-07T07:31:19Z
2016-10
Margarita, Favorskaya. Spatio-temporal Smoke Clustering in Outdoor Scenes Based on Boosted Random Forests [Текст] / Favorskaya Margarita, Pyataeva Anna, Popov Aleksei // Procedia Computer Science. — 2016. — Volume 96. — С. 762-771
http://www.sciencedirect.com/science/article/pii/S1877050916320415
https://elib.sfu-kras.ru/handle/2311/70020
Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.
Nowadays, vision-based techniques for automatic early smoke detection in the outdoor scenes are in a hot topic of computer vision. The basic set of features includes the traditional features describing the spatial ones, such as color, shape, transparency, energy, and fractal property, and the temporal ones, such as frame difference estimator, motion estimator, and flicker on boundaries. The main problem of the early smoke detection is to obtain the low values of the clustering errors. Our contribution deals with a reasonable clustering of the smoke/non-smoke regions based on the Boosted Random Forests (BRFs). The BRFs provide better clustering results in comparison with the traditional clustering techniques, as well as the ordinary random forests. Forty test video sequences with and without smoke were analyzed during experiments. The true recognition results of a smoke detection achieved 97.8% that is better on 3–4% of the results obtaining by the Support Vector Machine (SVM) application. False reject rate and false acceptance rate values were significantly decreased till 3.68% and 3.24% in average, respectively.
smoke detection
clustering
boosted random forests
spatial and temporal features
false alarm
Spatio-temporal Smoke Clustering in Outdoor Scenes Based on Boosted Random Forests
Journal Article
Journal Article Preprint
762-771
20.19.29
2018-02-07T07:31:19Z
10.1016/j.procs.2016.08.231
Институт космических и информационных технологий
Кафедра систем искусственного интеллекта
Procedia Computer Science
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