Fire detection based on vision sensor and support vector machines Adviser: Li Yu-Chiang Speaker: Wu Wei-Cheng Date: 2009/03/10 Fire Safety Journal, Volume 44, Issue 3, April 2009, Pages Byoung Chul Ko, Kwang-Ho Cheong, Jae-Yeal Nam 1

Outline 1. Introduction 2. Candidate fire-pixel detection Fire-colored pixel detection Fire-colored pixel detection Moving pixel detection using frame difference Moving pixel detection using frame difference Non-fire pixel removal using temporal luminance variation Non-fire pixel removal using temporal luminance variation 3. Fire-pixel verification using SVM 4. Experimental results 5. Conclusions 2

1. Introduction 3

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 Fire-colored pixel detection 5 2. Candidate fire-pixel detection

 Moving pixel detection using frame difference  In our experiment, a threshold t shows similar detection results and processing time when it is 6 2. Candidate fire-pixel detection

 Non-fire pixel removal using temporal luminance variation 7 2. Candidate fire-pixel detection

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 SVM (support vector machines) 9 3. Fire-pixel verification using SVM

 假設在 2D 裡 plot 6 個 trainning data, A(1,2), B(2,6), C(3,4), 這三個點的 data 為 (-1) 類別, D(1, - 1), E(2, -1), F(3, 2) 為 ( +1) 類別, 並假設 x-y = 0 是這個 hyperplane. ( 不一定正確, 但可一刀分割 ), x-y = -1 為 (-1) 類別的 boundary, A, C 兩點為此線上的 support vectors. x-y = 1 為 ( +1) 類別的 boundary, F 點則是 support vector 如果要知道 G(-3, 1) 與 H(3, 1) 是屬於哪一類的, 將 G 座標代入 hyperplane 中可得 = -4 G 點屬 (-1) 類別, 同理 將 H 座標代入 hyperplane 中可得 = 2 > 0 ==> H 點屬 ( +1) 類別 Fire-pixel verification using SVM

 Given training data that are vectors in space and their labels where, the general form of the binary linear classification function is Fire-pixel verification using SVM

12 4. Experimental results

13 4. Experimental results

5. Conclusions  The proposed approach is more robust to noise, such as smoke, and subtle differences between consecutive frames compared to previous research.  Computation time for fire detection needs to be improved to design a real-time fire-warning system. 14