An adaptive human action recognition system based on two-layer AP

Xing Li; Enqing Chen

doi:10.1117/12.2502908

9 August 2018 An adaptive human action recognition system based on two-layer AP

Xing Li, Enqing Chen

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061X (2018) https://doi.org/10.1117/12.2502908
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Xing Li and Enqing Chen "An adaptive human action recognition system based on two-layer AP", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061X (9 August 2018); https://doi.org/10.1117/12.2502908

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KEYWORDS

Databases

Detection and tracking algorithms

Feature extraction

3D modeling

RGB color model

Video

Video surveillance

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