Temporal Alignment of Human Behavior

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Abstract

Alignment of time series is an important problem to solve in many scientific disciplines. In particular, temporal alignment of two or more subjects performing similar activities is a challenging problem due to the large temporal scale difference between human actions as well as the inter/intra subject variability. In this paper we present canonical time warping (CTW), an extension of canonical correlation analysis (CCA) for spatio-temporal alignment of human motion between two subjects. CTW extends previous work on CCA in two ways: (i) it combines CCA with dynamic time warping (DTW), and (ii) it extends CCA by allowing local spatial deformations. We show CTW’s effectiveness in three experiments: alignment of synthetic data, alignment of motion capture data of two subjects per- forming similar actions, and alignment of similar facial expressions made by two people. Our results demonstrate that CTW provides both visually and qualitatively better alignment than state-of-the-art techniques based on DTW.

Citation

Paper thumbnail Feng Zhou and Fernando de la Torre
Canonical Time Warping for Alignment of Human Behavior
Neural Information Processing Systems (NIPS), 2009.
[PDF] [Poster, 5M] [Bibtex]

Results

CMU Multi-Modal Activity Database: CMU Motion Capture Database

Acknowledgements and Funding

This research is supported by:

Copyright notice

Human Sensing Lab