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Download fileAligned Cluster Analysis for Temporal Segmentation of Human Motion
journal contribution
posted on 2008-01-01, 00:00 authored by Feng Zhou, Fernando De la Torre, Jessica K. HodginsTemporal 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 k-
means 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.