A4 Conference proceedings

A Practical Hybrid Active Learning Approach for Human Pose Estimation

Open Access publication

Publication Details

Authors: Kaplan Sinan, Juvonen Joni, Lensu Lasse

Publisher: Springer Verlag (Germany): Series

Publication year: 2021

Language: English

Related journal or series: Lecture Notes in Computer Science

Title of parent publication: Structural, Syntactic, and Statistical Pattern Recognition

Journal acronym: LNCS

Volume number: 12644

ISBN: 978-3-030-73972-0

eISBN: 978-3-030-73973-7

ISSN: 0302-9743

eISSN: 1611-3349

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1007/978-3-030-73973-7_32

Open Access: Open Access publication

Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2021050629042


Active learning (AL) has not received much attention in deep learning (DL) for human pose estimation. In this paper, a practical hybrid active learning strategy is proposed for training a human pose estimation model, and it is tested in an industrial online environment. The conducted experiments show that the active learning strategy to select diverse samples to be annotated outperforms the baseline method with random sampling. As a result, the strategy enables a significant improvement in the performance of pose estimation.

Last updated on 2021-06-05 at 13:26