person-detection-raisinghand-recognition-0001

Use Case and High-Level Description

This is an action detector for the Smart Classroom scenario. It is based on the RMNet backbone that includes depth-wise convolutions to reduce the amount of computations for the 3x3 convolution block. The first SSD head from 1/16 scale feature map has four clustered prior boxes and outputs detected persons (two class detector). The second SSD-based head predicts actions of the detected persons. Possible actions: raising hand and other.

Example

person-detection-raisinghand-recognition-0001.png

Specification

Metric Value
Detector AP (internal test set 2) 81.50%
Accuracy (internal test set 2) 94.93%
Pose coverage Sitting, standing, raising hand
Support of occluded pedestrians YES
Occlusion coverage <50%
Min pedestrian height 80 pixels (on 1080p)
GFlops 7.138
MParams 1.951
Source framework Caffe*

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

Performance

Inputs

Name: input, shape: [1x3x400x680] - An input image in the format [BxCxHxW], where:

Expected color order is BGR.

Outputs

The net outputs four branches:

  1. name: mbox_loc1/out/conv/flat, shape: [b, num_priors*4] - Box coordinates in SSD format
  2. name: mbox_main_conf/out/conv/flat/softmax/flat, shape: [b, num_priors*2] - Detection confidences
  3. name: mbox/priorbox, shape: [1, 2, num_priors*4] - Prior boxes in SSD format
  4. name: out/anchor1, shape: [b, 2, h, w] - Action confidences
  5. name: out/anchor2, shape: [b, 2, h, w] - Action confidences
  6. name: out/anchor3, shape: [b, 2, h, w] - Action confidences
  7. name: out/anchor4, shape: [b, 2, h, w] - Action confidences

Where:

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