Human Activity Recognition using Deep Learning

M. Jeyapradha , A. Raja Soorya

DMLSmart Actions, Human Activity Recognition, Convolutional Neural Network, Human Image Threshing, Facial Image Threshing.

Vision-based human activity recognition in privately owned spaces and public places has become a significant issue in terms of developing safety feature to avoid theft like crimes. This technology improves security of privately owned spaces and public places. Deep learning models aim to automate extraction of low-level to high-level features of input data using convolutional feature instead of using aged complicated extraction methods. Convolutional feature has achieved significant improvements in classification of large amount of data especially in vision-based datasets. In order to recognize human action in smart environment, DMLSmartActions dataset is used for monitoring human activities. In deep learning, convolutional neural networks (CNNs) architecture is a class of deep neural networks commonly used to analyze visual imagery. The performance of the proposed method has been compared with previous methods that have used traditional machine learning methods on the same dataset. Experimental results demonstrated that the proposed deep learning model has achieved 82.41% accuracy rate in the classification of human activity which is far better than traditional machine learning method.

Published Paper   E-Certificate

"Human Activity Recognition using Deep Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 1, page no.1295 - 1297, January-2023, Available :https://ijsdr.org/papers/IJSDR2301209.pdf

Volume 8 Issue 1, January-2023

Pages : 1295 - 1297

Paper Reg. ID: IJSDR_203822

Published Paper Id: IJSDR2301209

Downloads: 000347224

Research Area: Engineering

Country: -, -, -

Published Paper PDF: https://ijsdr.org/papers/IJSDR2301209

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2301209

DOI: http://doi.one/10.1729/Journal.32956

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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