Smoke detection for surveillance cameras based on color, motion, and shape

This paper presents a smoke detection approach for surveillance cameras that uses color, shape, and motion characteristics. The fact a camera is immovable simplifies detection task by applying background subtraction. Color analysis emphasizes moving objects that have higher probability to be actual smoke. Due to limited performance of background subtraction, a real smoke region is represented as many separate pixels. These pixels are combined using density-based spatial clustering of applications with noise method and morphological operations. Shape of smoke candidate is evaluated using boundary roughness and area variability. Irregular density of smoke can be checked by edge density. The dynamic nature of smoke is confirmed by motion analysis. Tests on various datasets have shown consistency of the method.