Generalized Canonical Time Warping
Temporal alignment of human motion has been of recent interest due to its applications in animation, tele-rehabilitation and activity recognition. This paper presents generalized canonical time warping (GCTW), an extension of dynamic time warping (DTW) and canonical correlation analysis (CCA) for te...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 38(2016), 2 vom: 21. Feb., Seite 279-94 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2016
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. |
| Zusammenfassung: | Temporal alignment of human motion has been of recent interest due to its applications in animation, tele-rehabilitation and activity recognition. This paper presents generalized canonical time warping (GCTW), an extension of dynamic time warping (DTW) and canonical correlation analysis (CCA) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GCTW extends previous work on DTW and CCA in several ways: (1) it combines CCA with DTW to align multi-modal data (e.g., video and motion capture data); (2) it extends DTW by using a linear combination of monotonic functions to represent the warping path, providing a more flexible temporal warp. Unlike exact DTW, which has quadratic complexity, we propose a linear time algorithm to minimize GCTW. (3) GCTW allows simultaneous alignment of multiple sequences. Experimental results on aligning multi-modal data, facial expressions, motion capture data and video illustrate the benefits of GCTW. The code is available at http://humansensing.cs.cmu.edu/ctw |
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| Beschreibung: | Date Completed 17.10.2016 Date Revised 30.12.2016 published: Print Citation Status MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2015.2414429 |