Your browser doesn't support javascript.
Novel social distancing detector using local binary pattern in comparison with principal component analysis to improve accuracy
AIP Conference Proceedings ; 2655, 2023.
Artículo en Inglés | Scopus | ID: covidwho-20245510
ABSTRACT
The objective is to detect Novel Social Distancing using Local Binary Pattern (LBP) in comparison with Principal Component Analysis (PCA). Social Distance deduction is performed using Local Binary Pattern(N=20) and Principal Component Analysis(N=20) algorithms. Google AI open Images dataset is used for image detection. Dataset contains more than 10,000 images. Accuracy of Principal Component Analysis is 89.8% and Local Binary Pattern is 93.9%. There exists a statistical significant difference between LBP and PCA with (p<0.05). Local Binary Pattern appears to perform significantly better than Principal Component Analysis for Social Distancing Detection. © 2023 Author(s).
Palabras clave

Texto completo: Disponible Colección: Bases de datos de organismos internacionales Base de datos: Scopus Idioma: Inglés Revista: AIP Conference Proceedings Año: 2023 Tipo del documento: Artículo

Similares

MEDLINE

...
LILACS

LIS


Texto completo: Disponible Colección: Bases de datos de organismos internacionales Base de datos: Scopus Idioma: Inglés Revista: AIP Conference Proceedings Año: 2023 Tipo del documento: Artículo