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首页> 外文会议>International Conference on Computational Science and Its Applications - ICCSA 2003 Pt.3 May 18-21, 2003 Montreal, Canada >Human Motion Tracking by Combining View-Based and Model-Based Methods for Monocular Video Sequences
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Human Motion Tracking by Combining View-Based and Model-Based Methods for Monocular Video Sequences

机译：通过结合基于视图和基于模型的方法对单眼视频序列进行人体运动跟踪

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摘要

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines view-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classify individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on parameter optimization. We convert the human motion tracking problem into a parameter optimization problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.
机译：对运动的人进行可靠的跟踪对于运动估计，视频监视和人机界面至关重要。本文提出了一种结合基于视图和基于模型的技术的人体运动跟踪新方法。单目彩色视频在像素级别和对象级别都进行处理。在像素级别，使用高斯混合模型来训练和分类各个像素颜色。在对象级别，投影在2D图像平面上的3D人体模型用于拟合图像数据。由于奇异性问题，我们的方法不使用逆运动学。尽管许多其他方法使用随机采样进行基于模型的运动跟踪，但我们的方法完全依赖于参数优化。我们将人体运动跟踪问题转换为参数优化问题。用于参数优化的成本函数用于估计前景输入图像轮廓和投影的3D模型人体轮廓之间的重叠程度。通过将一组像素从图像域转换为真实投影平面域中的多边形，使用计算几何来计算重叠。我们的方法用于识别各种人体运动。视频序列的运动跟踪结果非常令人鼓舞。

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