三维运动序列中的人体动作识别

Konstantinos Kelgeorgiadis, N. Nikolaidis
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引用次数: 1

摘要

在本文中,我们提出了一种基于动态二进制体积(基于体素)或三维网格运动数据学习和识别人类动作的方法。人体在每个3D姿势中的方向是通过检测其脚来估计的,这些信息用于以一致的方式确定所有姿势的方向。对训练数据的三维姿态空间应用K-means,发现特征运动模式即三维动态。然后,利用模糊矢量量化(FVQ)在三维动力学空间中表示每个三维姿态,然后结合所有时间实例的信息来表示整个动作序列。然后应用线性判别分析(LDA)。实际的分类步骤使用支持向量机(SVM)。在一个三维动作数据库上的实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human action recognition in 3D motion sequences
In this paper we propose a method for learning and recognizing human actions on dynamic binary volumetric (voxel-based) or 3D mesh movement data. The orientation of the human body in each 3D posture is estimated by detecting its feet and this information is used to orient all postures in a consistent manner. K-means is applied on the 3D postures space of the training data to discover characteristic movement patterns namely 3D dynemes. Subsequently, fuzzy vector quantization (FVQ) is utilized to represent each 3D posture in the 3D dynemes space and then information from all time instances is combined to represent the entire action sequence. Linear discriminant analysis (LDA) is then applied. The actual classification step utilizes support vector machines (SVM). Results on a 3D action database verified that the method can achieve good performance.
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