Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/82195
| Title: | Skeleton-based action recognition with key-segment descriptor and temporal step matrix model | Authors: | Li, RM Fu, H Lo, WL Chi, ZR Song, ZX Wen, DS |
Issue Date: | 2019 | Source: | IEEE access, 20 Nov. 2019, v. 7, p. 169782-169795 | Abstract: | Human action recognition based on skeleton has played a key role in various computer visionrelated applications, such as smart surveillance, human-computer interaction, and medical rehabilitation. However, due to various viewing angles, diverse body sizes, and occasional noisy data, etc., this remains a challenging task. The existing deep learning-based methods require long time to train the models and may fail to provide an interpretable descriptor to code the temporal-spatial feature of the skeleton sequence. In this paper, a key-segment descriptor and a temporal step matrix model are proposed to semantically present the temporal-spatial skeleton data. First, a skeleton normalization is developed to make the skeleton sequence robust to the absolute body size and initial body orientation. Second, the normalized skeleton data is divided into skeleton segments, which are treated as the action units, combining 3D skeleton pose and the motion. Each skeleton sequence is coded as a meaningful and characteristic key segment sequence based on the key segment dictionary formed by the segments from all the training samples. Third, the temporal structure of the key segment sequence is coded into a step matrix by the proposed temporal step matrix model, and the multiscale temporal information is stored in step matrices with various steps. Experimental results on three challenging datasets demonstrate that the proposed method outperforms all the hand-crafted methods and it is comparable to recent deep learning-based methods. | Keywords: | Skeleton-based action recognition View alignment Scale normalization Key-segment descriptor Temporal step matrix model |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2019.2954744 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ The following publication R. Li, H. Fu, W. Lo, Z. Chi, Z. Song and D. Wen, "Skeleton-Based Action Recognition With Key-Segment Descriptor and Temporal Step Matrix Model," in IEEE Access, vol. 7, pp. 169782-169795, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2954744 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Li_Skeleton-Based_Action_Recognition.pdf | 2.56 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
47
Last Week
2
2
Last month
Citations as of May 28, 2023
Downloads
52
Citations as of May 28, 2023
SCOPUSTM
Citations
7
Citations as of Jun 2, 2023
WEB OF SCIENCETM
Citations
6
Citations as of Jun 1, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.