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.



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.


Name: input, shape: [1x3x320x544] - An input image in the format [BxCxHxW], where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

Expected color order is BGR.


The net outputs blob with shape: [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.

Legal Information

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