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2019 | OriginalPaper | Chapter

Stacked Mixed-Scale Networks for Human Pose Estimation

Authors : Xuan Wang, Zhi Li, Yanan Chen, Peilin Jiang, Fei Wang

Published in: PRICAI 2019: Trends in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Human pose estimation is an important problem in computer vision, which has been dominated by deep learning techniques in recent years. In this paper, we propose a novel model, named Mixed-Scale Dense Block, that exploits dilation convolution layers and dense concatenation connections to maximise the information flow through the block. Consequently, it captures the feature representation in different scales more effectively and efficiently. Comparing with the baseline method, Hourglass models, our model employs fewer learning parameters. Nevertheless, experiments demonstrate that the proposed model produces more accurate predictions. Meanwhile, our method achieves the comparable accuracy to state-of-the-art techniques. Especially in some indicators, our approach has better performance. In addition, this model is easy to implement and could be improved by most existing techniques that are adopted to promote the hourglass models.

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Title: Stacked Mixed-Scale Networks for Human Pose Estimation
Authors: Xuan Wang
Zhi Li
Yanan Chen
Peilin Jiang
Fei Wang
Publisher: Springer International Publishing
Book: PRICAI 2019: Trends in Artificial Intelligence
Print ISBN: 978-3-030-29907-1

Electronic ISBN: 978-3-030-29908-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-29908-8_18

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