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.

• Publication year

  2008

• Venue

  2008 Fourth International Conference on Natural Computation

• Publication date

  2008-10-18

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/ICNC.2008.219
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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