Overview:

Paper makes changes to the method in Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. Aim of the paper has been to reduce network parameters heavily, enable inference in realtime even on a CPU machine. Inference time of OpenPose is ~2.4s, while this approach yeilds ~5-10 fps (200-100ms) on web-cam feed using their python model at input resolution of 256x256.
Older OpenPose model

Changes Suggested

  • Uses lighter backbone, VGG16 -> MobileNetV1
  • Make single branch for PAF and Heatmaps prediction
  • Replace expensive 7x7 Conv's with 3x3, 1x1 and 3x3 with dilation=2 Conv blocks
New Architecture:

Results

  • Good performance of 43.4% of Average Precision with only just 1 refinement stage

Comparision with other backbones:

Review

  • If we reduce the network input resolution further to 128x128, gives great results !
  • Without drop on accuracy on major keypoints, performs ~>10fps on a CPU

Links: