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 1/8 and 1/16 scale feature maps 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: sitting, writing, raising hand, standing, turned around, lie on the desk.



Metric Value
Detector AP (internal test set 2) 90.70%
Accuracy (internal test set 2) 80.74%
Pose coverage sitting, writing, raising_hand, standing,
turned around, lie on the desk
Support of occluded pedestrians YES
Occlusion coverage <50%
Min pedestrian height 80 pixels (on 1080p)
GFlops 8.225
MParams 2.001
Source framework TensorFlow*

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: ActionNet/out_detection_loc, shape: b, num_priors*4 - Box coordinates in SSD format
  2. name: ActionNet/out_detection_conf, shape: b, num_priors*2 - Detection confidences
  3. name: ActionNet/action_heads/out_head_1_anchor_1, shape: b, 6, 50, 86 - Action confidences
  4. name: ActionNet/action_heads/out_head_2_anchor_1, shape: b, 6, 25, 43 - Action confidences
  5. name: ActionNet/action_heads/out_head_2_anchor_2, shape: b, 6, 25, 43 - Action confidences
  6. name: ActionNet/action_heads/out_head_2_anchor_3, shape: b, 6, 25, 43 - Action confidences
  7. name: ActionNet/action_heads/out_head_2_anchor_4, shape: b, 6, 25, 43 - Action confidences


  • b - batch size
  • num_priors - number of priors in SSD format (equal to 50x86x1+25x43x4=8600)

Legal Information

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