Global Context for Convolutional Pose Machines

TL;DR

This paper enhances convolutional pose machines with a global context module, achieving state-of-the-art accuracy in pose estimation while maintaining high speed, and demonstrates its effectiveness across multiple datasets.

Contribution

Introduces a U-shaped global context module for convolutional pose machines, improving accuracy and speed over existing methods in articulated pose estimation.

Findings

Achieves 87.9% PCKh on the LIP dataset.

Faster than hourglass-based networks with similar accuracy.

Global context integration improves pose estimation performance.

Abstract

Convolutional Pose Machine is a popular neural network architecture for articulated pose estimation. In this work we explore its empirical receptive field and realize, that it can be enhanced with integration of a global context. To do so U-shaped context module is proposed and compared with the pyramid pooling and atrous spatial pyramid pooling modules, which are often used in semantic segmentation domain. The proposed neural network achieves state-of-the-art accuracy with 87.9% PCKh for single-person pose estimation on the Look Into Person dataset. A smaller version of this network runs more than 160 frames per second while being just 2.9% less accurate. Generalization of the proposed approach is tested on the MPII benchmark and shown, that it faster than hourglass-based networks, while provides similar accuracy. The code is available at https://github.com/opencv/openvino_training_extensions/tree/develop/pytorch_toolkit/human_pose_estimation .