Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose

Daniil Osokin

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Paper

Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose

Topics: Deep Learning and Neural Networks; Image and Video Analysis and Understanding; Motion Tracking and Action Recognition

In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 744-748, 2019 , Prague, Czech Republic

Author: Daniil Osokin

Affiliation: Intel and Russian Federation

Keyword(s): Human Pose Estimation, Keypoints, Joints, Bottom-up, OpenPose, Real-time.

Abstract: In this work we adapt multi-person pose estimation architecture to use it on edge devices. We follow the bottom-up approach from OpenPose (Cao et al., 2017), the winner of COCO 2016 Keypoints Challenge, because of its decent quality and robustness to number of people inside the frame. With proposed network design and optimized post-processing code the full solution runs at 28 frames per second (fps) on Intel® NUC 6i7KYB mini PC and 26 fps on Core i7-6850K CPU. The network model has 4.1M parameters and 9 billions floating-point operations (GFLOPs) complexity, which is just ∼15% of the baseline 2-stage OpenPose with almost the same quality. The code and model are available as a part of Intel® OpenVINOTM Toolkit.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Osokin, D. (2019). Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 744-748. DOI: 10.5220/0007555407440748

@conference{icpram19,
author={Daniil Osokin.},
title={Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={744-748},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007555407440748},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
SN - 978-989-758-351-3
IS - 2184-4313
AU - Osokin, D.
PY - 2019
SP - 744
EP - 748
DO - 10.5220/0007555407440748
PB - SciTePress