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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2015.2414429
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|a eng
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|a Zhou, Feng
|e verfasserin
|4 aut
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|a Generalized Canonical Time Warping
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|c 2016
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|a Date Completed 17.10.2016
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|a Date Revised 30.12.2016
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a 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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|a Journal Article
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|a De la Torre, Fernando
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|t IEEE transactions on pattern analysis and machine intelligence
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|g 38(2016), 2 vom: 21. Feb., Seite 279-94
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