A Global to Local Double Embedding Method for Multi-person Pose Estimation

TL;DR

This paper introduces a novel global-to-local double embedding method for multi-person pose estimation that simplifies the process by integrating detection and joint estimation, improving accuracy and efficiency.

Contribution

It proposes a unified double embedding approach combining global and local features, along with a mutual refine machine for better joint prediction in complex scenarios.

Findings

Achieves competitive results on MSCOCO, MPII, and CrowdPose datasets.

Effectively reduces prediction difficulty in complex scenarios.

Demonstrates improved accuracy and generalization over existing methods.

Abstract

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method.