Human activity recognition based on an amalgamation of CEV & SGM features
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 11, 2022
- Source ID
- 10.3233/jifs-213514
Entities
People
- Kashif Kifayat
- Khush Bakhat
- M. Mattah Islam
- M. Shujah Islam
Organizations
- Air University
- Anhui Agricultural University
- National University of Computer and Emerging Sciences