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person-detection-retail-0013

Use Case and High-Level Description

This is a pedestrian detector for the Retail scenario. It is based on MobileNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block. The single SSD head from 1/16 scale feature map has 12 clustered prior boxes.

Example

Specification

Metric

Value

AP

88.62%

Pose coverage

Standing upright, parallel to image plane

Support of occluded pedestrians

YES

Occlusion coverage

<50%

Min pedestrian height

100 pixels (on 1080p)

GFlops

2.300

MParams

0.723

Source framework

Caffe*

Average Precision (AP) is defined as an area under the precision/recall curve.

Inputs

Image, name: data, shape: 1, 3, 320, 544 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

The net outputs blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. Each detection has the format [image_id, label, conf, x_min, y_min, x_max, y_max], where:

  • image_id - ID of the image in the batch

  • label - predicted class ID (1 - person)

  • conf - confidence for the predicted class

  • (x_min, y_min) - coordinates of the top left bounding box corner

  • (x_max, y_max) - coordinates of the bottom right bounding box corner

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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