Extracting relevant features from videos for a robust smoke detection

Abstract

In this paper, we propose a novel smoke detector based on relevant spatio-temporal features that depict the smoke’s dynamic appearance. Since smoke is a dynamic texture that can also be partially transparent, its detection involves two steps. First, moving pixels are detected using an adaptive background subtraction technique. Then, spatio-temporal features, measuring color and texture changes due to smoke in the underlying scene, are exploited to robustly recognize smoke regions. The novelty consists in addressing this two-class classification task by an entropy-based combination of two complementary classifiers using appropriate color and texture features. Furthermore, a sample-based background modeling with a bag-of-visual words representation makes the smoke detection not only discriminant but also robust against outdoor conditions. Experimental results indicate that our method exhibits a good robustness under challenging conditions.