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

In this article, we implement an action recognition technique based on features fusion and best feature selection. In the proposed method, HSI color transformation is performed in the first step to improve the contrast of video frames and then extract their motion features by optical flow algorithm. The frames fusion approach extracts the moving regions that find out by optical flow. After that, extract shape and texture features fused by a new parallel approach name length control features. A new Weighted Entropy-Variances approach is applied to a combined vector and selects the best of them for classification. Finally, features are passed in M-SVM for final features classification into relevant human actions. The experimental process is conducted in four famous action datasets-Weizmann, KTH, UCF Sports, and UCF YouTube, with recognition rate 97.9%, 100%, 99.3%, and 94.5%, respectively. Experimental results show that the proposed scheme performed significantly sound output concerning listed methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This article proposes an action recognition technique based on features fusion and best feature selection, achieving high recognition rates on multiple famous datasets. The experimental results demonstrate the superior performance of the proposed scheme compared to listed methods.