Motion boundary based sampling and 3D co-occurrence descriptors for action recognition

作者：

Highlights：

• A motion boundary based sampling strategy is proposed for dense trajectory.

• A set of 3D co-occurrence descriptors is developed to describe cuboids.

• Two decomposition strategies are presented to further improve performance.

• We achieve state-of-the-art results on several human action datasets.

摘要

•A motion boundary based sampling strategy is proposed for dense trajectory.•A set of 3D co-occurrence descriptors is developed to describe cuboids.•Two decomposition strategies are presented to further improve performance.•We achieve state-of-the-art results on several human action datasets.

论文关键词：Dense trajectory,Action recognition,3D co-occurrence descriptors,Motion boundary,Bag of Features

论文评审过程：Received 21 August 2013, Revised 21 February 2014, Accepted 26 June 2014, Available online 3 July 2014.

论文官网地址：https://doi.org/10.1016/j.imavis.2014.06.011