Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Sam Johnson,Mark Everingham +1 more
- pp 1-11
TLDR
A new annotated database of challenging consumer images is introduced, an order of magnitude larger than currently available datasets, and over 50% relative improvement in pose estimation accuracy over a state-of-the-art method is demonstrated.Abstract:
We investigate the task of 2D articulated human pose estimation in unconstrained still images This is extremely challenging because of variation in pose, anatomy, clothing, and imaging conditions Current methods use simple models of body part appearance and plausible configurations due to limitations of available training data and constraints on computational expense We show that such models severely limit accuracy Building on the successful pictorial structure model (PSM) we propose richer models of both appearance and pose, using state-of-the-art discriminative classifiers without introducing unacceptable computational expense We introduce a new annotated database of challenging consumer images, an order of magnitude larger than currently available datasets, and demonstrate over 50% relative improvement in pose estimation accuracy over a stateof-the-art methodread more
Citations
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Proceedings ArticleDOI
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
TL;DR: Part Affinity Fields (PAFs) as discussed by the authors uses a nonparametric representation to learn to associate body parts with individuals in the image and achieves state-of-the-art performance on the MPII Multi-Person benchmark.
Book ChapterDOI
Stacked Hourglass Networks for Human Pose Estimation
TL;DR: This work introduces a novel convolutional network architecture for the task of human pose estimation that is described as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions.
Posted Content
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
TL;DR: This work presents an approach to efficiently detect the 2D pose of multiple people in an image using a nonparametric representation, which it refers to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image.
Journal ArticleDOI
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
TL;DR: OpenPose as mentioned in this paper uses Part Affinity Fields (PAFs) to learn to associate body parts with individuals in the image, which achieves high accuracy and real-time performance.
Proceedings ArticleDOI
Convolutional Pose Machines
TL;DR: In this paper, a convolutional network is incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation, which can implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation.
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Proceedings ArticleDOI
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