基于特征融合的背景差分改进算法

doi:10.16180/j.cnki.issn1007-7820.2020.12.005

Abstract

Abstract:

The background difference method has higher reliability in completely and quickly segmenting the target image, but the detection effect is not good in the case of background disturbance and illumination variation. This study proposes an improved background difference algorithm based on feature fusion. This algorithm fuses spatio-temporal local binary pattern texture features and color features, and consideres the confidence and similarity scores of the two features to obtain the background probability, following by foreground segmentation. The background pixels are used for background template updates, which is designed to better solve the problem of target detection in complex backgrounds. The experimental results show that the detection effect of this algorithm is better than other similar algorithms. While maintaining the robustness and complexity of the background difference algorithm, it displayes good detection effect under the background disturbance and illumination changes.

Key words: background subtraction, target detection, feature fusion, confidence, background probability, template update

CLC Number:

TP391.41

GUAN Hongyun,SU Zhentao,WANG Chen. Improved Background Subtraction Based on Feature Fusion[J].Electronic Science and Technology, 2020, 33(12): 22-27.

Figures/Tables 5

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References 16

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Method	Blizzard		Cubilce		winterStreet		Badminton		Canoe		Overpass		Average
Method	CT	HT	CT	HT	CT	HT	CT	HT	CT	HT	CT	HT	CT	HT
GMM	145.1	32	101.6	9	133.4	26	113.1	36	43.8	8	111.9	8	107.6	19.8
KDE	101.2	45	118.2	12	203.8	31	129.5	42	38.1	13	140.5	13	121.9	26.0
PBAS	275.7	56	136.4	19	259.5	51	147.6	60	71.6	18	158.9	16	174.9	36.7
本文算法	103.4	41	110.7	10	196.3	29	135.9	44	32.5	11	120.8	9	116.6	24.0

Method	Blizzard		Cubilce		WinterStreet		Badminton		Canoe		Overpass		Average
Method	R	P	R	P	R	P	R	P	R	P	R	P	R	P
GMM	0.81	0.52	0.81	0.59	0.74	0.42	0.79	0.70	0.76	0.86	0.73	0.82	0.77	0.65
KDE	0.78	0.73	0.83	0.55	0.71	0.35	0.73	0.62	0.75	0.83	0.80	0.91	0.76	0.67
PBAS	0.72	0.87	0.86	0.67	0.66	0.38	0.76	0.64	0.86	0.90	0.83	0.92	0.78	0.73
本文算法	0.80	0.74	0.90	0.74	0.75	0.56	0.83	0.85	0.80	0.87	0.87	0.92	0.83	0.78

Method	Blizzard	Cubilce	WinterStreet	Badminton	Canoe	Overpass
GMM	0.63	0.68	0.53	0.74	0.80	0.77
KDE	0.72	0.66	0.47	0.67	0.78	0.85
PBAS	0.79	0.75	0.48	0.69	0.88	0.87
本文算法	0.76	0.81	0.64	0.84	0.83	0.89

Improved Background Subtraction Based on Feature Fusion

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