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: standing, writing, demonstrating.



Metric Value
Detector AP (internal test set 2) 80.0%
Accuracy (internal test set 1) 72.4%
Pose coverage Standing, writing, demonstrating
Support of occluded pedestrians YES
Occlusion coverage <50%
Min pedestrian height 80 pixels (on 1080p)
GFlops 7.140
MParams 1.951
Source framework Caffe*

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


Image, name: input, shape: 1, 3, 400, 680 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.


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, 3, h, w - Action confidences
  5. name: out/anchor2, shape: b, 3, h, w - Action confidences
  6. name: out/anchor3, shape: b, 3, h, w - Action confidences
  7. name: out/anchor4, shape: b, 3, h, w - Action confidences


  • b - batch size
  • num_priors - number of priors in SSD format (equal to 25x43x4=4300)
  • h, w - height and width of the output feature map (h=25, w=43)

Legal Information

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