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Proceedings Paper

Skeleton-based human action recognition using multiple sequence alignment
Author(s): Wenwen Ding; Kai Liu; Fei Cheng; Jin Zhang; YunSong Li
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Paper Abstract

Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.

Paper Details

Date Published: 21 May 2015
PDF: 8 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010N (21 May 2015); doi: 10.1117/12.2180828
Show Author Affiliations
Wenwen Ding, Xidian Univ. (China)
Huaibei Normal Univ. (China)
Kai Liu, Xidian Univ. (China)
Fei Cheng, Xidian Univ. (China)
Jin Zhang, Xidian Univ. (China)
YunSong Li, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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