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.



  1. name: "input" , shape: [1x400x680x3] - An input image in the format [BxHxWxC], where:

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

    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


Legal Information

[*] Other names and brands may be claimed as the property of others.