A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

作者：

Highlights：

• A sparse activation function is applied to find out the locations of active regions.

• Fused two segmented frames using multiplication law of probability.

• Features are fused using a parallel approach name length control features (LCF).

• Weighted Entropy-Variance controlled approach is proposed for features selection.

摘要

•A sparse activation function is applied to find out the locations of active regions.•Fused two segmented frames using multiplication law of probability.•Features are fused using a parallel approach name length control features (LCF).•Weighted Entropy-Variance controlled approach is proposed for features selection.

论文关键词：Pre-processing,Frames fusion,Features extraction,Features fusion,Features selection,Classification

论文评审过程：Received 23 June 2020, Revised 16 November 2020, Accepted 7 December 2020, Available online 10 December 2020, Version of Record 22 December 2020.

论文官网地址：https://doi.org/10.1016/j.imavis.2020.104090