Fast Human Pose Estimation

Abstract

Existing human pose estimation approaches often only consider how to improve the model generalisation performance, but putting aside the significant efficiency problem. This leads to the development of heavy models with poor scalability and cost-effectiveness in practical use. In this work, we investigate the under-studied but practically critical pose model efficiency problem. To this end, we present a new Fast Pose Distillation (FPD) model learning strategy. Specifically, the FPD trains a lightweight pose neural network architecture capable of executing rapidly with low computational cost. It is achieved by effectively transferring the pose structure knowledge of a strong teacher network. Extensive evaluations demonstrate the advantages of our FPD method over a broad range of state-of-the-art pose estimation approaches in terms of model cost-effectiveness on two standard benchmark datasets, MPII Human Pose and Leeds Sports Pose.

arXiv commentAccepted by CVPR2019

References in corpus (7)

• Distilling the Knowledge in a Neural Network
• DeepPose: Human Pose Estimation via Deep Neural Networks
• Do Deep Nets Really Need to be Deep?
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• Human pose estimation via Convolutional Part Heatmap Regression
• Knowledge Distillation by On-the-Fly Native Ensemble
• Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations

Cited by in corpus (1)

• RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation