EfficientPose: Scalable single-person pose estimation
- URL: http://arxiv.org/abs/2004.12186v2
- Date: Fri, 4 Dec 2020 09:27:44 GMT
- Title: EfficientPose: Scalable single-person pose estimation
- Authors: Daniel Groos, Heri Ramampiaro, Espen A. F. Ihlen
- Abstract summary: We propose a novel convolutional neural network architecture, called EfficientPose, for single-person pose estimation.
Our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets.
Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost.
- Score: 3.325625311163864
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Single-person human pose estimation facilitates markerless movement analysis
in sports, as well as in clinical applications. Still, state-of-the-art models
for human pose estimation generally do not meet the requirements of real-life
applications. The proliferation of deep learning techniques has resulted in the
development of many advanced approaches. However, with the progresses in the
field, more complex and inefficient models have also been introduced, which
have caused tremendous increases in computational demands. To cope with these
complexity and inefficiency challenges, we propose a novel convolutional neural
network architecture, called EfficientPose, which exploits recently proposed
EfficientNets in order to deliver efficient and scalable single-person pose
estimation. EfficientPose is a family of models harnessing an effective
multi-scale feature extractor and computationally efficient detection blocks
using mobile inverted bottleneck convolutions, while at the same time ensuring
that the precision of the pose configurations is still improved. Due to its low
complexity and efficiency, EfficientPose enables real-world applications on
edge devices by limiting the memory footprint and computational cost. The
results from our experiments, using the challenging MPII single-person
benchmark, show that the proposed EfficientPose models substantially outperform
the widely-used OpenPose model both in terms of accuracy and computational
efficiency. In particular, our top-performing model achieves state-of-the-art
accuracy on single-person MPII, with low-complexity ConvNets.
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