vehicle-attributes-recognition-barrier-0039

Use Case and High-Level Description

This model presents a vehicle attributes classification algorithm for a traffic analysis scenario.

Example

_images/vehicle-attributes-recognition-barrier-0039-1.png

Specification

Metric

Value

Car pose

Front facing cars

Occlusion coverage

<50%

Min object width

72 pixels

Supported colors

White, gray, yellow, red, green, blue, black

Supported types

Car, bus, truck, van

GFlops

0.126

MParams

0.626

Source framework

Caffe*

Accuracy - Confusion Matrix

Color accuracy, %

blue

gray

yellow

green

black

white

red

blue

79.53

4.32

0.62

6.41

6.54

2.47

0.12

gray

2.53

78.01

0

1.36

1.18

16.74

0.18

yellow

0

13.9

54.01

11.21

0

10.7

10.16

green

3.79

1.52

1.52

83.33

6.06

3.03

0.76

black

0.85

1.92

0

0.32

96.1

0.74

0.07

white

1.45

10.86

0.17

2.53

0.08

84.83

0.08

red

0.89

0.3

2.18

2.18

0.3

1.88

92.27

Color average accuracy: 81.15 %

Type accuracy, %

car

van

truck

bus

car

98.26

0.56

0.98

0.2

van

3.72

89.16

6.15

0.97

track

1.71

2.46

94.27

1.56

bus

7.94

3.8

19.69

68.57

Type average accuracy: 87.56 %

Inputs

Image, name: input, shape: 1, 3, 72, 72 in the format 1, C, H, W, where:

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Outputs

  1. name: color, shape: 1, 7, 1, 1 - Softmax output across seven color classes [white, gray, yellow, red, green, blue, black]

  2. name: type, shape: 1, 4, 1, 1 - Softmax output across four type classes [car, bus, truck, van]

Use Case and High-Level Description

This model presents a vehicle attributes classification algorithm for a traffic analysis scenario.

Example

_images/vehicle-attributes-recognition-barrier-0039-1.png

Specification

Metric

Value

Car pose

Front facing cars

Occlusion coverage

<50%

Min object width

72 pixels

Supported colors

White, gray, yellow, red, green, blue, black

Supported types

Car, bus, truck, van

GFlops

0.126

MParams

0.626

Source framework

Caffe*

Accuracy - Confusion Matrix

Color accuracy, %

blue

gray

yellow

green

black

white

red

blue

79.53

4.32

0.62

6.41

6.54

2.47

0.12

gray

2.53

78.01

0

1.36

1.18

16.74

0.18

yellow

0

13.9

54.01

11.21

0

10.7

10.16

green

3.79

1.52

1.52

83.33

6.06

3.03

0.76

black

0.85

1.92

0

0.32

96.1

0.74

0.07

white

1.45

10.86

0.17

2.53

0.08

84.83

0.08

red

0.89

0.3

2.18

2.18

0.3

1.88

92.27

Color average accuracy: 81.15 %

Type accuracy, %

car

van

truck

bus

car

98.26

0.56

0.98

0.2

van

3.72

89.16

6.15

0.97

track

1.71

2.46

94.27

1.56

bus

7.94

3.8

19.69

68.57

Type average accuracy: 87.56 %

Inputs

Image, name: input, shape: 1, 3, 72, 72 in the format 1, C, H, W, where:

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: BGR.

Outputs

  1. name: color, shape: 1, 7, 1, 1 - Softmax output across seven color classes [white, gray, yellow, red, green, blue, black]

  2. name: type, shape: 1, 4, 1, 1 - Softmax output across four type classes [car, bus, truck, van]

Legal Information

[*] Other names and brands may be claimed as the property of others.