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

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 %

Performance (FPS)

Inputs

  1. name: "input" , shape: [1x3x72x72] - An input image in following format [1xCxHxW], 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.