Abstract

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 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, the Carnegie Mellon Motion Capture database and several action databases, demonstrate its effectiveness.

Results

Publications

Aligned Cluster Analysis for Temporal Segmentation of Human Motion
Feng Zhou, Fernando de la Torre and Jessica K. Hodgins
International Conference on Automatic Face and Gesture Recognition (FG), 2008
[Paper 1Mb] [Poster 4Mb]