Video Flame Detection Algorithm Based on Improved GMM and Multi-Feature Fusion

Chi Zhang; Qinghao Meng; Tao Jing

doi:10.3788/LOP202158.0410006

Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0410006(2021)

Video Flame Detection Algorithm Based on Improved GMM and Multi-Feature Fusion

Chi Zhang, Qinghao Meng, and Tao Jing^*

Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Process Detection and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

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Figures & Tables(12)

Fig. 1. Detection flowchart based on improved GMM and multi-feature fusion

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Fig. 2. Frame capture of flame video sequence. (a) Indoor flame; (b) outdoor flame; (c) forest flame

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Fig. 3. Original and foreground renderings.(a)(b) Original image; (c)(d) traditional GMM foreground extraction map; (e)(f) improved GMM foreground extraction map

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Fig. 4. Feature extraction flowchart

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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)

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Fig. 6. Experimental videos. (a) Outdoor flame; (b) indoor flame; (c) forest flame; (d) walking pedestrians; (e) flashing car lights; (f) flashing neon lights

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Table 1. Comparison of algorithm processing speed unit: ms/frame
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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 value
GMM 44.5 47.8 49.3 47.2
Improved GMM 33.7 30.4 31.6 31.9

Table 2. Comparison of false alarms
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Table 2. Comparison of false alarms
Algorithm Fig. 2(a) Fig. 2(b) Fig. 2(c) Average
GMM 1233 674 521 809
TBGMM 426 587 403 472
IGMM 269 391 137 266

Table 3. Comparison of missed inspections
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Table 3. Comparison of missed inspections
Algorithm Fig. 2(a) Fig. 2(b) Fig. 2(c) Average
GMM 563 601 731 632
TBGMM 411 398 759 523
IGMM 518 489 501 503

Table 4. Target similarity comparison

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Table 4. Target similarity comparison

Target	1	2	3	4	5	6	7	Average value
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

Table 5. Comparison of flame video detection results

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Table 5. Comparison of flame video detection results

Video	Videoframe	Fireframe	Ref. [6]		Ref. [9]		Ref. [5]		Proposed method
Video	Videoframe	Fireframe	MF	TP /%	MF	TP /%	MF	TP /%	MF	TP /%
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

Table 6. Comparison of non-fire video detection results

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Table 6. Comparison of non-fire video detection results

Video	Videoframe	Fireframe	Ref. [6]		Ref. [9]		Ref. [5]		Proposed method
Video	Videoframe	Fireframe	FF	FN /%	FF	FN /%	FF	FN /%	FF	FN /%
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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Chi Zhang, Qinghao Meng, Tao Jing. Video Flame Detection Algorithm Based on Improved GMM and Multi-Feature Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410006

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