Unsupervised Smoke Detection in Normally Smoking Environments

The problem of smoke detection through visual analytics is an open challenging problem. The existing literature has addressed the problem by mainly working on the best feature representation and by exploiting supervised solutions which consider the problem of smoke detection as a binary classification one. Differently from such works, we consider the possibility that in some contexts sensing smokes is a common situation, but want to detect when there are significative fluctuations within this normal situation. In light of such a consideration, we propose an unsupervised solution that leverages on the concept of anomaly detection. Different visual representations have been used together with a temporal smoothing function reduce the effects of noisy measurement. Such temporally smoothed representations are then exploited to learn a robust "normality" model by means of a One-Class Support Vector Machine. A real prototype has been developed and exploited to collect a new dataset which has been considered to evaluate the proposed solution.