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| Format: | Recurso digital |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.19333715 |
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| _version_ | 1866901079701061632 |
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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 |