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FGR
2008
IEEE

Aligned Cluster Analysis for temporal segmentation of human motion

9 years 4 months ago
Aligned Cluster Analysis for temporal segmentation of human motion
Temporal segmentation of human motion into actions is a crucial step for understanding and building computational models of human motion. Several issues contribute to the challenge of this task. These include the large variability in the temporal scale and periodicity of human actions, as well as the exponential nature of all possible movement combinations. We formulate the temporal segmentation problem as an extension of standard clustering algorithms. In particular, this paper proposes Aligned Cluster Analysis (ACA), a robust method to temporally segment streams of motion capture data into actions. ACA extends standard kernel kmeans clustering in two ways: (1) the cluster means contain a variable number of features, and (2) a dynamic time warping (DTW) kernel is used to achieve temporal invariance. Experimental results, reported on synthetic data and the Carnegie Mellon Motion Capture database, demonstrate its effectiveness.
Feng Zhou, Fernando De la Torre, Jessica K. Hodgin
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where FGR
Authors Feng Zhou, Fernando De la Torre, Jessica K. Hodgins
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