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An adaptive human action recognition system based on two-layer AP
Author(s): Xing Li; Enqing Chen
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Paper Abstract

Skeleton-based methods have been proposed to detect and recognize meaningful human motion. It is known that most of them must contain some parameters. To achieve better recognition performance, various evolutionary schemes have been applied to select the optimal parameters in each phase of these human recognition methods. Experimental evaluations of various parameters, in terms of action recognition performance, should be done for obtaining the optimal parameter. In this paper, we propose an adaptive skeleton-based human action recognition system which can automatically adjust the experimental parameters according to the input data. We first extract some spatiotemporal local features by obtaining position differences of joints, which models actions over time. Then a two-layer affinity propagation (AP) algorithm is employed to select crucial postures. Our experiment results demonstrates that the proposed method works well for different dataset.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061X (9 August 2018); doi: 10.1117/12.2502908
Show Author Affiliations
Xing Li, Zhengzhou Univ. (China)
Enqing Chen, Zhengzhou Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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