Browsing by Author "Agirman, Ahmet Kerim"

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Karanlık Videolarda Orman Yangını Tespitine Yönelik Uzaysal-Zamansal Nesne Öznitelikleri
(2022) Agirman, Ahmet Kerim; Tasdemir, Kasim
In this paper, a wildfire detection algorithm from dark videos is proposed. Unlike the daytime wildfires, in the dark videos, neither the fire nor its surrounding has visually clearly perceptible texture. Its unique visual characteristics make it challenging to extract descriptive object features. This paper addresses the challenging problem by tracking the glowing objects in the darkness and extracting features based on the spatio-temporal behavior of them. It is experimentally shown that the proposed features are descriptive enough to classify wildfires with over 90% accuracy even there exists deceptive light sources such as city lights, flashlights, car headlights and reflections in the scene. Moreover, we investigate several conventional machine learning algorithms such as ensemble and kernel-based methods on the same spatio-temporal feature set. Comprehensive empirical test results demonstrate that the most accurate detection is obtained when the spatio-temporal feature set is classified using Random Forest.