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Proceedings Paper

Skeleton based action recognition using pose change map and convolutional neural networks
Author(s): Boxiang Hou Sr.; Guohui Tian Sr.; Bin Huang Sr.
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Paper Abstract

Recent skeleton-based action recognition approaches have achieved significant improvement by using convolutional neural networks. These methods usually map skeleton sequences into images, and an end-to-end CNN is adopted for label prediction. In this paper, a novel image mapping method is proposed, named pose change map (PCM), which provides a visual indication of how the similarity between human pose and atoms of pose dictionary changes over time. Then, PCM as well as raw skeleton coordinates are fed into CNN to extract robust and discriminative features for action recognition. Experiments on two challenging datasets NTU RGB+D and UTKinect-Action consistently demonstrate the superiority of our method.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791L (14 August 2019); doi: 10.1117/12.2539634
Show Author Affiliations
Boxiang Hou Sr., Shandong Univ. (China)
Guohui Tian Sr., Shandong Univ. (China)
Bin Huang Sr., Shandong Univ. (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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