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human-pose-estimation-0005

Use Case and High-Level Description

This is a multi-person 2D pose estimation network based on the EfficientHRNet approach (that follows the Associative Embedding framework). For every person in an image, the network detects a human pose: a body skeleton consisting of keypoints and connections between them. The pose may contain up to 17 keypoints: ears, eyes, nose, shoulders, elbows, wrists, hips, knees, and ankles.

Example

Specification

Metric

Value

Average Precision (AP)

45.6%

GFlops

5.9206

MParams

8.1506

Source framework

PyTorch*

Average Precision metric described in COCO Keypoint Evaluation site.

Inputs

Image, name: image, shape: 1, 3, 288, 288 in the B, C, H, W format, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

The net outputs are two blobs:

  1. heatmaps of shape 1, 17, 144, 144 containing location heatmaps for keypoints of all types. Locations that are filtered out by non-maximum suppression algorithm have negated values assigned to them.

  2. embeddings of shape 1, 17, 144, 144, 1 containing associative embedding values, which are used for grouping individual keypoints into poses.

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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