Efficient unsupervised temporal segmentation of human motion
Efficient unsupervised temporal segmentation of human motion
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DOI：
10.2312/sca.20141135
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发表时间：
2014-07
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通讯作者：
Anna Vögele;Björn Krüger;R. Klein
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文献类型：
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作者：
Anna Vögele;Björn Krüger;R. Klein
本文介绍了一种有效的方法，用于人体运动序列和相似时间序列的全自动时间分割。该方法依赖于一个邻域图分割成不同的活动和运动基元的输入序列中给定的自相似结构的给定的数据序列。特别地，在所发现的活动段内的重复的快速检测是针对运动分析和合成的任何运动处理流水线的关键问题。邻域图中相同的相似性信息被进一步利用，以将这些原语聚类成具有语义意义的更大实体。然后，将经过这种分类的元素用作先验，用于估计完全未知的数据流的相同目标值。该技术不对手头的运动序列做任何假设，也不需要用户交互来进行分割或聚类。我们的技术进行测试的CMU和HDM 05运动捕捉数据库上展示了我们的系统处理运动分割，聚类，运动合成和转移的标签问题在实践中的能力-后者是一个可选的步骤，依赖于预先存在的一个小的标记数据集。
This work introduces an efficient method for fully automatic temporal segmentation of human motion sequences and similar time series. The method relies on a neighborhood graph to partition a given data sequence into distinct activities and motion primitives according to self-similar structures given in that input sequence. In particular, the fast detection of repetitions within the discovered activity segments is a crucial problem of any motion processing pipeline directed at motion analysis and synthesis. The same similarity information in the neighborhood graph is further exploited to cluster these primitives into larger entities of semantic significance. The elements subject to this classification are then used as prior for estimating the same target values for entirely unknown streams of data. The technique makes no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. Tests of our techniques are conducted on the CMU and HDM05 motion capture databases demonstrating the capability of our system handling motion segmentation, clustering, motion synthesis and transfer-of-label problems in practice - the latter being an optional step which relies on the preexistence of a small set of labeled data.