Moving Object Detection Under Rain and Snow Weather Conditions

Guoliang Yang; Dingling Yu; Yang Wang; Yanfang Wang

doi:10.3788/LOP57.241507

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 24 >Page 241507 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 24, 241507 (2020)

Moving Object Detection Under Rain and Snow Weather Conditions

Guoliang Yang, Dingling Yu^*, Yang Wang, and Yanfang Wang

Author Affiliations

School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China

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DOI: 10.3788/LOP57.241507 Cite this Article Set citation alerts

Guoliang Yang, Dingling Yu, Yang Wang, Yanfang Wang. Moving Object Detection Under Rain and Snow Weather Conditions[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241507 Copy Citation Text

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Fig. 1. Framework of proposed algorithm

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Qualitative comparison results of different sequences. Blizzard sequence (a) 1096th frame images; (b) 1111th frame images; snowfall sequence (c) 807th frame images; (d) 832th frame images; sleet sequence (e) 680th frame images, (f) 688th frame images; (g) 3249th frame images in blizzard sequence; (h) 5692th frame images in skating sequence; (i) snow sequence; (j) ship sequence

Fig. 2. Qualitative comparison results of different sequences. Blizzard sequence (a) 1096^th frame images; (b) 1111^th frame images; snowfall sequence (c) 807^th frame images; (d) 832^th frame images; sleet sequence (e) 680^th frame images, (f) 688^th frame images; (g) 3249^th frame images in blizzard sequence; (h) 5692^th frame images in skating sequence; (i) snow sequence; (j) ship sequence

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Fig. 3. Influence of different parameters on measured value of F. (a) λ; (b) μ

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Fig. 4. Comparison results of different constraints on different sequences. Snowfall sequence (a) 807^th frame images, (b) 832^th frame images; wet snow sequence (c) 680^th frame images, (d) 688^th frame images

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Image	DECOLOR			TVRPCA			DNLRl1TV			HOSVD			OSTD			Proposed algorithm
Image	P	R	F	P	R	F	P	R	F	P	R	F	P	R	F	P	R	F
Fig.2(a)	0.36	0.96	0.57	0.70	0.47	0.57	0.60	0.61	0.61	0.53	0.71	0.61	0.63	0.69	0.66	0.75	0.85	0.80
Fig.2(b)	0.63	0.34	0.44	0.76	0.26	0.38	0.73	0.67	0.70	0.42	0.80	0.55	0.78	0.62	0.69	0.83	0.81	0.82
Fig.2(c)	0.76	0.57	0.65	0.83	0.51	0.63	0.78	0.60	0.68	0.56	0.70	0.62	0.66	0.69	0.68	0.84	0.78	0.81
Fig.2(d)	0.70	0.79	0.75	0.72	0.71	0.71	0.78	0.81	0.79	0.43	0.47	0.45	0.40	0.43	0.41	0.81	0.89	0.88
Fig.2(e)	0.22	0.75	0.34	0.48	0.71	0.57	0.78	0.80	0.79	0.63	0.86	0.73	0.83	0.77	0.80	0.88	0.93	0.90
Fig.2(f)	0.65	0.73	0.69	0.72	0.78	0.74	0.72	0.79	0.75	0.65	0.80	0.72	0.65	0.83	0.73	0.91	0.94	0.92
Fig.2(g)	0.50	0.51	0.50	0.52	0.43	0.47	0.73	0.85	0.79	0.74	0.85	0.79	0.62	0.82	0.71	0.90	0.91	0.90

Table 1. Measurement values of three indicators of different algorithms

Algorithm	Fig.2(a)	Fig.2(b)	Fig.2(c)	Fig.2(d)	Fig.2(e)	Fig.2(f)	Fig.2(g)
Proposed algorithm	27.04	20.31	29.95	21.32	25.03	17.67	21.89
DECOLOR	57.83	46.12	67.90	62.98	71.87	62.95	69.03
TVRPCA	150.23	143.56	163.72	138.53	178.45	167.83	177.93
DNLRL1TV	34.32	19.42	28.43	27.23	36.53	33.38	37.93
HOSVD	62.04	52.42	73.63	62.59	71.58	62.66	58.29
OSTD	30.53	28.34	37.68	42.90	50.06	39.62	35.98

Table 2. Comparison of running time of 6 algorithms unit: s

Guoliang Yang, Dingling Yu, Yang Wang, Yanfang Wang. Moving Object Detection Under Rain and Snow Weather Conditions[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241507

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