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Key-Pose Prediction in Cyclic Human Motion

机译:循环人体运动中的关键姿态预测

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摘要

In this paper we study the problem of estimating innercyclic time intervalswithin repetitive motion sequences of top-class swimmers in a swimming channel.Interval limits are given by temporal occurrences of key-poses, i.e.distinctive postures of the body. A key-pose is defined by means of only one ortwo specific features of the complete posture. It is often difficult to detectsuch subtle features directly. We therefore propose the following method: Giventhat we observe the swimmer from the side, we build a pictorial structure ofposelets to robustly identify random support poses within the regular motion ofa swimmer. We formulate a maximum likelihood model which predicts a key-posegiven the occurrences of multiple support poses within one stroke. The maximumlikelihood can be extended with prior knowledge about the temporal location ofa key-pose in order to improve the prediction recall. We experimentally showthat our models reliably and robustly detect key-poses with a high precisionand that their performance can be improved by extending the framework withadditional camera views.
机译:在本文中,我们研究了在泳道中顶级游泳者重复运动序列中估计内循环时间间隔的问题。时间间隔由关键姿势的时间出现(即身体的独特姿势)给出。关键姿势仅通过完整姿势中的一个或两个特定特征来定义。通常很难直接检测到这些细微的特征。因此,我们提出以下方法:考虑到我们从侧面观察游泳者,我们构建了姿势图的结构来稳固地识别游泳者正常运动中的随机支撑姿势。我们制定了最大似然模型,该模型可以预测关键人物在一个笔划内出现多个支撑姿势的情况。可以利用关于关键姿势的时间位置的先验知识来扩展最大似然性,以改善预测召回率。我们通过实验证明了我们的模型能够可靠,可靠地以高精度检测关键位置,并且可以通过扩展带有其他相机视图的框架来提高其性能。

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