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Proceedings Paper

A spatiotemporal multiscale statistical matching (SMSM) model for human actions detection
Author(s): Jing Han; Junwei Zhu; Lianfa Bai; Jiang Yue
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Paper Abstract

Considering the noise, background interference and massive information, it is a challenging issue to recognize human actions from videos. We, in this paper, present a non-training spatiotemporal multiscale statistical matching (SMSM) model based on the dense computation of so-called spatiotemporal local adaptive regression kernel to identify non-compact human actions. Therefore, our model can avoid the overfitting problem caused by large sample training. First, we encode the local context similarity by exploiting Gaussian difference LARK (GLARK) features. This feature can well describe the shape and trend of the weak edge. Second, we propose multiscale composite template set in SMSM, whose robustness to the detection of variable human actions in different sizes. The proposed SMSM model can balance the relationship between GLARK structure of local small window and neighborhood structure of local large window. Moreover, our statistical process solves the problem of weak edge missed detection brought by background interference and promotes the multiscale matching efficiency. In our experiments, the proposed algorithm significantly outperforms existing matching methods and some supervised methods on the universally acknowledged challenging dataset.

Paper Details

Date Published: 9 August 2018
PDF: 17 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062K (9 August 2018); doi: 10.1117/12.2503328
Show Author Affiliations
Jing Han, Nanjing Univ. of Science and Technology (China)
Junwei Zhu, Nanjing Univ. of Science and Technology (China)
Lianfa Bai, Nanjing Univ. of Science and Technology (China)
Jiang Yue, Nanjing Univ. of Science and Technology (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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