human-pose-estimation-0002

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) 44.4%
GFlops 5.9393
MParams 8.1504
Source framework PyTorch*

Average Precision metric described in COCO Keypoint Evaluation site.

Inputs

Name: input, shape: [1x3x288x288]. An input image in the [BxCxHxW] format , where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width Expected color order is BGR.

Outputs

The net outputs three blobs:

  • "heatmaps" of shape [N, 17, 144, 144] containing location heatmaps for keypoints of all types.
  • "nms_heatmaps" of shape [N, 17, 144, 144] containing heatmaps after non-maximum suppression.
  • "embeddings" of shape [N, 17, 144, 144, 1] containing associative embedding values, which are used for grouping individual keypoints into poses.

Legal Information

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