From handcrafted to learned representations for human action recognition: A survey

作者：

Highlights：

• We provide a comprehensive stud over the state-of-the-art action representations.

• Deep learning-based representations are compared to handcrafted representations.

• Pros and cons of current deep learning-based approaches are discussed.

摘要

•We provide a comprehensive stud over the state-of-the-art action representations.•Deep learning-based representations are compared to handcrafted representations.•Pros and cons of current deep learning-based approaches are discussed.

论文关键词：Human action recognition,Handcrafted features,Deep learning,Convolutional neural network,Dictionary learning

论文评审过程：Received 15 September 2015, Revised 7 May 2016, Accepted 16 June 2016, Available online 27 June 2016, Version of Record 10 November 2016.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.06.007