RMPE: Regional Multi-person Pose Estimation

Abstract

Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve a 17% increase in mAP over the state-of-the-art methods on the MPII (multi person) dataset.Our model and source codes are publicly available.

Models & Codes available at https://github.com/MVIG-SJTU/RMPE or https://github.com/Fang-Haoshu/RMPE

References in corpus (8)

• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• Stacked Hourglass Networks for Human Pose Estimation
• Convolutional Pose Machines
• An Implementation of Faster RCNN with Study for Region Sampling
• DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
• Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
• Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
• Multi-Person Pose Estimation with Local Joint-to-Person Associations

Cited by in corpus (8)

• Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
• LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking
• Generative Partition Networks for Multi-Person Pose Estimation
• Dual Path Networks for Multi-Person Human Pose Estimation
• Multi-person Articulated Tracking with Spatial and Temporal Embeddings
• Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation
• Bottom-up Pose Estimation of Multiple Person with Bounding Box Constraint
• Recurrent Residual Module for Fast Inference in Videos