human-pose-estimation-0001

Use Case and High-Level Description

This is a multi-person 2D pose estimation network (based on the OpenPose approach) with tuned MobileNet v1 as a feature extractor. It finds a human pose: body skeleton, which consists of keypoints and connections between them, for every person inside image. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees and ankles.

Example

human-pose-estimation-0001.png

Specification

Metric Value
Average Precision (AP) 42.8%
GFlops 15.435
MParams 4.099
Source framework Caffe*

Average Precision metric described in COCO Keypoint Evaluation site.

Tested on a COCO validation subset from the original paper: Cao et al. "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields".

Performance

Inputs

  1. name: "input" , shape: [1x3x256x456] - An input image in the format [BxCxHxW], where:
    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width. Expected color order is BGR.

Outputs

  1. The net outputs two blobs with shapes: [1, 38, 32, 57] and [1, 19, 32, 57]. The first blob contains keypoint pairwise relations (part affinity fields), the second one contains keypoint heatmaps.

Legal Information

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