Automatic Extraction of Semantic Action Features
Automatic Extraction of Semantic Action Features
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DOI：
10.1109/sitis.2013.35
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Tran Thang Thanh;Fan Chen;K. Kotani;H. Le
中科院分区：
文献类型：
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作者：
Tran Thang Thanh;Fan Chen;K. Kotani;H. Le
随着3D专用标记等技术的发展，我们可以捕获标记关节的运动信号并创建大量的3D动作MOCAP数据。我们对人类动作了解得越多，我们就越能将其应用到应用中，例如动作识别（安全）、动画（体育、3D卡通电影和虚拟世界）、体育分析、游戏等。为了找到人类动作的语义代表特征，我们提出了人体动作捕捉数据的语义标注方法，并使用关系特征概念自动提取一组动作特征作为空间信息。对于每个动作类别，我们提出了一种统计方法，以进一步从空间信息中提取公共集合作为上述时间序列。最终提取的知识用于识别动作。在我们的实验中，我们表明，通过这种方法提取的知识在识别测试数据集上的动作时仅需要很少的训练样本就达到了非常高的准确度。
With the development of the technology like 3D specialized markers, we could capture the moving signals from marker joints and create a huge set of 3D action MOCAP data. The more we understand the human action, the better we could apply it to applications, e.g., action recognition (security), animation (sport, 3D cartoon movies, and virtual world), analysis of sports, game etc. In order to find the semantically representative features of human actions, we propose the semantic annotation approach of the human motion capture data and use the relational feature concept to extract automatically a set of action features as spatial information. For each action class, we propose a statistical method to further extract the common sets as temporal sequences of above from spatial information. The final knowledge extracted is used to recognize the action. In our experiments, we show that the knowledge extracted by this method achieves very high accuracy in recognizing actions on testing data-set with only few of training samples.