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human-pose-estimation-0006

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

_images/human-pose-estimation-0006.png

Specification

Metric

Value

Average Precision (AP)

51.1%

GFlops

8.844

MParams

8.1506

Source framework

PyTorch*

Average Precision metric described in COCO Keypoint Evaluation site.

Inputs

Image, name: input, shape: 1, 3, 352, 352 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, 176, 176 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, 176, 176, 1 containing associative embedding values, which are used for grouping individual keypoints into poses.

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