Gesture recognition for human–machine interaction in table tennis video based on deep semantic understanding

作者：

Highlights：

• We add new topological information constraint to form a topological sparse encoder in the semi-supervised learning of video image features.

• By fine-tuning the network parameters, we are able to obtain more identifiable video sequence features.

摘要

•We add new topological information constraint to form a topological sparse encoder in the semi-supervised learning of video image features.•By fine-tuning the network parameters, we are able to obtain more identifiable video sequence features.

论文关键词：Video semantic learning,Gesture recognition,human–machine interaction,Table tennis,Topological information,Dynamic time warping

论文评审过程：Received 15 July 2019, Revised 21 October 2019, Accepted 3 November 2019, Available online 6 November 2019, Version of Record 13 November 2019.

论文官网地址：https://doi.org/10.1016/j.image.2019.115688