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首页> 外文期刊>Electronic Letters on Computer Vision and Image Analysis: ELCVIA >Finding Kinematic Structure in Time Series Volume Data
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Finding Kinematic Structure in Time Series Volume Data

机译：在时间序列体数据中查找运动学结构

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This paper presents a new scheme for acquiring 3D kinematic structure and motion from time series volume data. Our basic strategy is to first represent the shape structure of the target in each frame by Reeb graph which we compute by using geodesic distance of target’s surface, and then estimate the kinematic structure of the target which is consistent with these shape structures. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent to a certain extent of time series. Once we acquire a unique kinematic structure, we fit it to other Reeb graphs in the remaining frames, and describe the motion throughout the entire time series. The only assumption we make is that human body can be approximated by an articulated body with certain numbers of end-points and branches. We demonstrate the efficacy of the proposed scheme through some experiments. Key Words: Vision-Based Motion Capture, Video and Image Sequence Analysis, Reeb graph, Motion Tracking and Analysis.
机译：本文提出了一种从时间序列体数据中获取3D运动结构和运动的新方案。我们的基本策略是先通过Reeb图表示每一帧中目标的形状结构，然后利用目标表面的测地线距离进行计算，然后估算与这些形状结构一致的目标运动学结构。尽管帧之间的形状结构可能有很大不同，但我们建议通过对图的某些节点进行聚类来得出唯一的运动学结构，这是基于它们在一定程度上与时间序列保持一致的事实。一旦获得了独特的运动学结构，我们就将其与其余帧中的其他Reeb图拟合，并描述整个时间序列中的运动。我们做出的唯一假设是，人体可以被具有一定数量的端点和分支的关节运动体近似。我们通过一些实验证明了该方案的有效性。关键词：基于视觉的运动捕捉，视频和图像序列分析，Reeb图形，运动跟踪和分析。

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