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person-attributes-recognition-crossroad-0031.md

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person-attributes-recognition-crossroad-0031

Use Case and High-Level Description

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

Example

Attribute Value
is male true
has hat false
has longsleeves true
has longpants true
has longhair false
has coat_jacket false

Specification

Metric Value
Pedestrian pose Standing person
Occlusion coverage <20%
Min object width 80 pixels
Supported attributes gender, has hat, has longsleeves, has longpants, has longhair, has coat_jacket
GFlops 0.22
MParams 1.1
Source framework Caffe*

Accuracy

Attribute Precision Recall F1 Accuracy
Average 0.90 0.86 0.88 0.90
is_male 0.89 0.87 0.88 0.86
has_hat 0.67 0.35 0.46 0.97
has_longsleeves 0.92 0.93 0.96 0.94
has_longpants 0.97 0.96 0.96 0.94
has_longhair 0.80 0.73 0.76 0.91
has_coat_jacket 0.73 0.52 0.61 0.84

Inputs

  1. name: "input" , shape: [1x3x80x160] - An input image in following format [1xCxHxW], where

    • C - number of channels
      • H - image height
      • W - image width.

    The expected color order is BGR.

Outputs

  1. The net outputs a blob named sigmoid with shape: [1, 6, 1, 1] across six attributes: [is_male, has_hat, has_longsleeves, has_longpants, has_longhair, has_coat_jacket]. Value > 0.5 means that an attribute is present.

Legal Information

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