Human activity recognition based on an amalgamation of CEV & SGM features

Author:

Bakhat Khush1,Kifayat Kashif1,Islam M. Shujah2,Islam M. Mattah3

Affiliation:

1. Air University, Islamabad, Pakistan

2. Anhui Agricultural University, Hefei, Anhui, PR China

3. National University of Computer and Emerging Sciences, Islamabad, Pakistan

Abstract

The method of marking video clips with action symbols is known as vision-based human activity recognition. Robust solutions to this problem have a variety of practical implementations. Due to differences in motion performance, recording environments, and inter-personal differences, the challenge is difficult. We specifically resolve these problems in this study work, and we solve imitations of state-of-the-art research. Projected human activity recognition is based on an amalgamation of CEV & SGM features. The proposed solution outperforms current models and produces state-of-the-art outcomes as compared to the best effectiveness of the control, according to experimental results on the datasets.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference25 articles.

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