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2018 | OriginalPaper | Chapter

Improved Key Poses Model for Skeleton-Based Action Recognition

Authors : Xiaoqiang Li, Yi Zhang, Junhui Zhang

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

With the development of Kinect sensor, action recognition based on human skeleton becomes a prosperous research field. In this paper, an improved method is proposed to select a few inconsecutive, discriminative and ordinal frames (named Improved Key Poses) to represent a human skeleton action. The main contributions of the proposed method are summarized as follow. First, a novel Key Poses Mining method is presented to keep time order of each frame in action video. Second, we selected a new feature which could reflect the micro-motion of specific actions and increase the recognition accuracy. The proposed method is evaluated on three benchmark datasets: MSR Action3D dataset, UTKinect Action dataset and Florence 3D Action dataset. The experiment results show that the proposed approach outperforms than some state-of-the-art methods.

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Metadata
Title
Improved Key Poses Model for Skeleton-Based Action Recognition
Authors
Xiaoqiang Li
Yi Zhang
Junhui Zhang
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_35