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person-detection-action-recognition-0006

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.

Example

Specification

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.

Inputs

Image, name: input, shape: 1, 400, 680, 3 in the format B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order is BGR.

Outputs

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, 50, 85, 6 - Action confidences

  4. name: ActionNet/action_heads/out_head_2_anchor_1, shape: b, 25, 43, 6 - Action confidences

  5. name: ActionNet/action_heads/out_head_2_anchor_2, shape: b, 25, 43, 6 - Action confidences

  6. name: ActionNet/action_heads/out_head_2_anchor_3, shape: b, 25, 43, 6 - Action confidences

  7. name: ActionNet/action_heads/out_head_2_anchor_4, shape: b, 25, 43, 6 - Action confidences

Where:

  • b - batch size

  • num_priors - number of priors in SSD format (equal to 50x85x1+25x43x4=8550)

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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