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篇目详细内容 |
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【篇名】 |
Action recognition from arbitrary views using 3D-key-pose set |
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【刊名】 |
Frontiers of Electrical and Electronic Engineering |
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【刊名缩写】 |
Front. Electr. Electron. Eng. |
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【ISSN】 |
2095-2732 |
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【EISSN】 |
2095-2740 |
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【DOI】 |
10.1007/s11460-011-0175-6 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2012 |
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【卷期】 |
7
卷2期 |
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【页码】 |
224-241
页,共
18
页 |
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【作者】 |
Junxia GU;
Xiaoqing DING;
Shenjing WANG;
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【关键词】 |
action representation; action recognition; 3D-key-pose set; 3D key pose sequence; action models fusion |
【摘要】 |
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Recovering three-dimensional (3D) human pose sequence from arbitrary view is very difficult, due to loss of depth information and self-occlusion. In this paper, view-independent 3D-key-pose set is selected from 3D action samples, for the purpose of representing and recognizing those same actions from a single or few cameras without any restriction of the relative orientations between cameras and subjects. First, 3D-key-pose set is selected from the 3D human joint sequences of 3D training action samples that are built from multiple viewpoints. Second, 3D key pose sequence, which matches best with the observation sequence, is selected from the 3D-key-pose set to represent the observation sequence of arbitrary view. 3D key pose sequence contains many discriminative view-independent key poses but cannot accurately describe pose of every frame in the observation sequence. Considering the above reasons, pose and dynamic of action are modeled respectively in this paper. Exemplar-based embedding and probability of unique key pose are applied to model pose property. Complementary dynamic feature is extracted to model these actions that share the same poses but have different dynamic features. Finally, these action models are fused to recognize observation sequence from a single or few cameras. Effectiveness of the proposed approach is demonstrated with experiments on IXMAS dataset. |
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