A spatiotemporal multiscale statistical matching (SMSM) model for human actions detection

Jing Han; Junwei Zhu; Lianfa Bai; Jiang Yue

doi:10.1117/12.2503328

9 August 2018 A spatiotemporal multiscale statistical matching (SMSM) model for human actions detection

Jing Han, Junwei Zhu, Lianfa Bai, Jiang Yue

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062K (2018) https://doi.org/10.1117/12.2503328
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Jing Han, Junwei Zhu, Lianfa Bai, and Jiang Yue "A spatiotemporal multiscale statistical matching (SMSM) model for human actions detection", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062K (9 August 2018); https://doi.org/10.1117/12.2503328

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KEYWORDS

Video

Genetic algorithms

Detection and tracking algorithms

Target detection

Statistical modeling

3D modeling

Optical tracking

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