README.md

person-detection-action-recognition-0005

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: sitting, standing, raising hand.

Example

Specification

Metric	Value
Detector AP (internal test set 2)	80.0%
Accuracy (internal test set 2)	83.8%
Pose coverage	Sitting, standing, raising hand
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.

Inputs

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.

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, 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

Where:

• 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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