Hankelet-based dynamical systems modeling for 3D action recognition
作者：
Highlights：
• We model an action as sequence of outputs of linear time invariant (LTI) systems.
• We represent the outputs of LTI systems by means of Hankelets.
• We adopt an HMM to model the transitions from one LTI system to another.
• We formulate an inference and supervised learning formulation for our model.
• We also present a deep analysis of the parameter settings for our action representation.
摘要
•We model an action as sequence of outputs of linear time invariant (LTI) systems.•We represent the outputs of LTI systems by means of Hankelets.•We adopt an HMM to model the transitions from one LTI system to another.•We formulate an inference and supervised learning formulation for our model.•We also present a deep analysis of the parameter settings for our action representation.
论文关键词：Hidden Markov Model,Hankel Matrix,Linear time invariant system,Discriminative learning,Action
论文评审过程：Received 5 February 2015, Revised 27 July 2015, Accepted 2 September 2015, Available online 22 October 2015, Version of Record 2 November 2015.
论文官网地址：https://doi.org/10.1016/j.imavis.2015.09.007
