Temporal Alignment of Human Behavior
People
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
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:- Opening cabinet [Video 3M]
- Boxing [Video 3M]
Acknowledgements and Funding
This research is supported by:
Copyright notice
Human Sensing Lab |