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Table 1 The quantitative F-measure metric (%) of the compared BGS methods on CDnet [38] datasets

From: Fast 1-minimization algorithm for robust background subtraction

Dataset

Classic methods

Low-rank methods

Sparse methods

 

SOBS

ViBe

SuBS

DECO

MAMR

GOSUS

RePROCS

Xiao

Proposed

Office

96.63

90.32

97.02

95.34

85.23

91.54

90.87

89.61

94.31

PETS2006

85.26

84.32

85.12

79.13

77.63

78.21

79.33

75.16

86.16

Fountain01

11.21

6.05

15.63

2.71

6.35

7.55

8.36

7.15

8.61

Fountain02

85.81

63.38

84.69

75.36

77.65

70.23

67.93

78.38

83.44

Parking

36.68

45.33

72.76

34.61

58.03

30.84

40.37

59.71

75.31

Sofa

62.18

61.97

62.69

50.31

63.46

51.24

47.87

67.49

69.63

Cubicle

72.05

79.65

79.33

77.67

69.38

71.23

69.34

70.91

73.64

Copy Machine

57.21

81.71

89.74

78.18

70.68

79.22

74.32

69.61

75.61

Park

59.70

69.53

58.69

75.81

70.96

72.33

66.98

70.14

74.73

Dining Room

71.73

75.49

70.36

82.47

78.33

76.48

70.38

72.30

84.67

Snow fall

67.01

82.49

85.03

83.46

82.36

81.84

76.70

73.41

85.27

Skating

76.33

73.67

80.25

83.81

85.68

79.83

75.93

78.64

82.11

Tram Crossroad

74.18

85.64

71.36

74.62

76.69

75.65

67.33

71.49

75.29

Turnpike

78.98

90.64

89.67

88.37

85.44

85.45

79.43

79.61

86.82

Winter Street

51.04

30.58

55.56

66.13

49.64

38.45

33.20

57.91

60.34

Tram Station

71.32

70.69

72.91

69.54

70.03

72.98

68.26

61.41

76.17

Turbulence0

2.64

5.36

8.65

38.34

35.67

37.94

33.62

29.34

40.34

Turbulence3

74.96

65.48

80.28

77.67

81.36

68.29

59.33

79.56

87.64

Average

63.05

64.57

69.98

68.53

68.03

64.96

61.64

66.21

73.33

  1. Best, bold; second best, italics