Visual-Based Smoke Detection Using Support Vector Machine

Smoke detection becomes more and more appealing because of its important application in fire protection. In this paper, we suggest some more universal features, such as the changing unevenness of density distribution and the changing irregularities of the contour of smoke. In order to integrate these features reasonably and gain a low generalization error rate, we propose a support vector machine based smoke detector. The feature set and the classifier can be used in various smoke cases contrary to the limited applications of other methods. Experimental results on different styles of smoke in different scenes show that the algorithm is reliable and effective.