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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

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

  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.