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Main Authors: Ji-Hoon Kwon, Sung-Min Kim, Hyeong-Seok Kim, Young-Cheol Kim
Format: Recurso digital
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Published: Zenodo 2024
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Online Access:https://doi.org/10.5281/zenodo.19333715
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author Ji-Hoon Kwon
Sung-Min Kim
Hyeong-Seok Kim
Young-Cheol Kim
author_facet Ji-Hoon Kwon
Sung-Min Kim
Hyeong-Seok Kim
Young-Cheol Kim
contents <p>—In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object's motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_19333715
institution Zenodo
language
publishDate 2024
publisher Zenodo
record_format zenodo
spellingShingle Adaptive Computational Methods for Robust Object Tracking
Ji-Hoon Kwon
Sung-Min Kim
Hyeong-Seok Kim
Young-Cheol Kim
Corner detection
optical flow
epipolar geometry
RANSAC.
<p>—In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object's motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values</p>
title Adaptive Computational Methods for Robust Object Tracking
topic Corner detection
optical flow
epipolar geometry
RANSAC.
url https://doi.org/10.5281/zenodo.19333715