Aligned Cluster Analysis for Temporal Segmentation of Human Motion

Feng Zhou, Fernando De la Torre Frade, and Jessica K. Hodgins
IEEE Conference on Automatic Face and Gestures Recognition, September, 2008.


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

Notes
Associated Center(s) / Consortia: National Robotics Engineering Center
Associated Lab(s) / Group(s): Human Sensing and Advanced Mechatronics Lab
Associated Project(s): Temporal Segmentation of Human Motion

Text Reference
Feng Zhou, Fernando De la Torre Frade, and Jessica K. Hodgins, "Aligned Cluster Analysis for Temporal Segmentation of Human Motion," IEEE Conference on Automatic Face and Gestures Recognition, September, 2008.

BibTeX Reference
@inproceedings{Zhou_2008_6155,
   author = "Feng Zhou and Fernando {De la Torre Frade} and Jessica K Hodgins",
   title = "Aligned Cluster Analysis for Temporal Segmentation of Human Motion",
   booktitle = "IEEE Conference on Automatic Face and Gestures Recognition",
   month = "September",
   year = "2008",
}