Author Affiliations
Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Process Detection and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, Chinashow less
Fig. 1. Detection flowchart based on improved GMM and multi-feature fusion
Fig. 2. Frame capture of flame video sequence. (a) Indoor flame; (b) outdoor flame; (c) forest flame
Fig. 3. Original and foreground renderings.(a)(b) Original image; (c)(d) traditional GMM foreground extraction map; (e)(f) improved GMM foreground extraction map
Fig. 4. Feature extraction flowchart
Fig. 5. Original images and LBP histograms. (a)(b) Flame images; (c)(d) fire-like images; (e) histogram extracted from Fig. (a); (f) histogram extracted from Fig. (b); (g) histogram of extracted from Fig. (c); (h) histogram of extracted from Fig. (d)
Fig. 6. Experimental videos. (a) Outdoor flame; (b) indoor flame; (c) forest flame; (d) walking pedestrians; (e) flashing car lights; (f) flashing neon lights
Algorithm | Fig. 2(a) | Fig. 2(b) | Fig. 2(c) | Average value |
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GMM | 44.5 | 47.8 | 49.3 | 47.2 | Improved GMM | 33.7 | 30.4 | 31.6 | 31.9 |
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Table 1. Comparison of algorithm processing speed unit: ms/frame
Algorithm | Fig. 2(a) | Fig. 2(b) | Fig. 2(c) | Average |
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GMM | 1233 | 674 | 521 | 809 | TBGMM | 426 | 587 | 403 | 472 | IGMM | 269 | 391 | 137 | 266 |
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Table 2. Comparison of false alarms
Algorithm | Fig. 2(a) | Fig. 2(b) | Fig. 2(c) | Average |
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GMM | 563 | 601 | 731 | 632 | TBGMM | 411 | 398 | 759 | 523 | IGMM | 518 | 489 | 501 | 503 |
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Table 3. Comparison of missed inspections
Target | 1 | 2 | 3 | 4 | 5 | 6 | 7 | Average value |
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Flame | 0.7020 | 0.7764 | 0.6986 | 0.7356 | 0.7204 | 0.8016 | 0.7932 | 0.7468 | Sunrise | 0.5075 | 0.5108 | 0.5596 | 0.5845 | 0.5655 | 0.6019 | 0.4912 | 0.5459 | Headlight | 0.2301 | 0.2422 | 0.2499 | 0.3047 | 0.3881 | 0.1963 | 0.2943 | 0.2722 | Candle flame | 0.9457 | 0.8536 | 0.8983 | 0.8203 | 0.8915 | 0.8294 | 0.8231 | 0.8660 |
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Table 4. Target similarity comparison
Video | Videoframe | Fireframe | Ref. [6] | Ref. [9] | Ref. [5] | Proposed method |
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MF | TP /% | MF | TP /% | MF | TP /% | MF | TP /% |
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Fig. 6(a) | 208 | 198 | 6 | 92.31 | 9 | 90.87 | 10 | 90.38 | 4 | 93.27 | Fig. 6(b) | 368 | 350 | 5 | 93.75 | 4 | 94.02 | 8 | 92.93 | 2 | 94.57 | Fig. 6(c) | 217 | 199 | 14 | 85.25 | 1 | 91.24 | 15 | 84.79 | 6 | 88.94 | Average | -- | -- | -- | 90.44 | -- | 92.04 | -- | 89.37 | -- | 92.26 |
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Table 5. Comparison of flame video detection results
Video | Videoframe | Fireframe | Ref. [6] | Ref. [9] | Ref. [5] | Proposed method |
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FF | FN /% | FF | FN /% | FF | FN /% | FF | FN /% |
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Fig. 6(d) | 148 | 0 | 5 | 3.38 | 3 | 2.03 | 4 | 2.70 | 3 | 2.03 | Fig. 6(e) | 187 | 0 | 9 | 4.81 | 12 | 6.42 | 10 | 5.35 | 3 | 1.60 | Fig. 6(f) | 273 | 0 | 15 | 5.49 | 5 | 1.83 | 18 | 6.59 | 10 | 3.66 | Average | -- | -- | -- | 4.56 | -- | 3.43 | -- | 4.88 | -- | 2.43 |
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Table 6. Comparison of non-fire video detection results