Comparing random forest approaches to segmenting and classifying gestures

作者：

Highlights：

• Sequential and simultaneous random forest frameworks are compared.

• Fusing skeletal and appearance features enables accurate gesture representations.

• Uniform descriptors are created for gestures to account for variability in length.

摘要

•Sequential and simultaneous random forest frameworks are compared.•Fusing skeletal and appearance features enables accurate gesture representations.•Uniform descriptors are created for gestures to account for variability in length.

论文关键词：Gesture spotting,Gesture classification,Random forest classifier

论文评审过程：Received 1 October 2015, Revised 30 May 2016, Accepted 5 June 2016, Available online 11 June 2016, Version of Record 20 February 2017.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.06.001