Randomized time warping for motion recognition

作者：

Highlights：

• Tackle issues in dynamic time warping by time elastic features and subspace method

• At conceptual level, extend DTW by multiple similarity measurements

• In implementation, generalize the Hankel matrix

• Able to generate rich temporal variation from one sample

• Easy to implement yet outperform conventional methods

摘要

•Tackle issues in dynamic time warping by time elastic features and subspace method•At conceptual level, extend DTW by multiple similarity measurements•In implementation, generalize the Hankel matrix•Able to generate rich temporal variation from one sample•Easy to implement yet outperform conventional methods

论文关键词：Feature extraction,Dynamic time warping,Subspace method,Hankel matrix,Motion recognition

论文评审过程：Received 8 May 2015, Revised 30 May 2016, Accepted 13 July 2016, Available online 25 July 2016, Version of Record 2 August 2016.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.07.003