28 January 2010 Cigarette smoke detection from captured image sequences
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We investigate a detection of smoke from captured image sequences. We propose to address the following two problems in order to attain this goal. The first problem is to estimate candidate areas of smoke. The second problem is to judge if smoke exists in the scene. To solve the first problem, we apply the previously proposed framework where image sequences are divided into some small blocks and the smoke detection is done in each small block. In this framework, we propose to use color and edge information of the scene. To solve the second problem, we propose a method for judging if smoke exists in the scene by using the areas of smoke obtained in the last step part. We propose some feature values for judging if smoke exists in the scene. Then, by simulation we find the best combination of feature values. In addition, we study the effect of normalization, which provide better performance in recognition.
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Kentaro Iwamoto, Kentaro Iwamoto, Hironori Inoue, Hironori Inoue, Toru Matsubara, Toru Matsubara, Toshihisa Tanaka, Toshihisa Tanaka, } "Cigarette smoke detection from captured image sequences", Proc. SPIE 7538, Image Processing: Machine Vision Applications III, 753813 (28 January 2010); doi: 10.1117/12.840133; https://doi.org/10.1117/12.840133

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