Tracking articulated hand motion with eigen dynamics analysis

Proceedings Ninth IEEE International Conference on Computer Vision Pub Date : 2003-10-13 DOI:10.1109/ICCV.2003.1238472

Hanning Zhou, Thomas S. Huang

引用次数: 123

Abstract

This paper introduces the concept of eigen-dynamics and proposes an eigen dynamics analysis (EDA) method to learn the dynamics of natural hand motion from labelled sets of motion captured with a data glove. The result is parameterized with a high-order stochastic linear dynamic system (LDS) consisting of five lower-order LDS. Each corresponding to one eigen-dynamics. Based on the EDA model, we construct a dynamic Bayesian network (DBN) to analyze the generative process of a image sequence of natural hand motion. Using the DBN, a hand tracking system is implemented. Experiments on both synthesized and real-world data demonstrate the robustness and effectiveness of these techniques.

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基于特征动力学分析的关节手运动跟踪

介绍了特征动力学的概念，提出了一种特征动力学分析(EDA)方法，从数据手套捕获的标记运动集中学习手部自然运动的动力学。结果用一个由5个低阶随机线性动力系统组成的高阶随机线性动力系统(LDS)参数化。每个对应一个本征动力学。在EDA模型的基础上，构建了一个动态贝叶斯网络(DBN)来分析手部自然运动图像序列的生成过程。利用DBN，实现了一个手部跟踪系统。在合成数据和实际数据上的实验证明了这些技术的鲁棒性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings Ninth IEEE International Conference on Computer Vision

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