Evaluation of Robust Covariance Estimation for Object Detection

Fecha

2021-04-07

Autores

Tamayo-Arango, Andres Felipe
Plazas Escudero, David
Vidal-Correa, Juan Pablo

Título de la revista

ISSN de la revista

Título del volumen

Editor

Universidad EAFIT

Resumen

This work presents an initial approach to the evaluation of robust covariance estimation for object detection (localization) using the “region covariance” technique from the literature. The covariance estimation is performed using the Comedian, Kendall, Spearman and Ledoit and Wolf robust approaches for covariance, and the procedure was also compared using two different matrix norms for estimating dissimilarity. The performance was measured quantitatively using linear regression and Pareto boundaries, yielding the Ledoit and Wolf estimation with best overall performance in object detection in normal and noisy images.

Descripción

Palabras clave

Citación