Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines
In view of the complex and changeable environment in underground coal mines, an improved algorithm based on the principal component analysis-scale invariant feature transform (PCA-SIFT) and mean shift is proposed to address the issues for which existing tracking algorithms are not adequate; for exam...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8661756/ |
| _version_ | 1818641538554527744 |
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| author | Jiang Dai-Hong Dai Lei Li Dan Zhang San-You |
| author_facet | Jiang Dai-Hong Dai Lei Li Dan Zhang San-You |
| author_sort | Jiang Dai-Hong |
| collection | DOAJ |
| description | In view of the complex and changeable environment in underground coal mines, an improved algorithm based on the principal component analysis-scale invariant feature transform (PCA-SIFT) and mean shift is proposed to address the issues for which existing tracking algorithms are not adequate; for example, when differentiating between moving targets and the background, the tracking in the case of moving objects (e.g., confusion between foreground and background) is not optimal. This results in poor resolution and the inability to deal with very dusty conditions, scale change, and rotation. The proposed feature target tracking model was developed using the scale invariance property of the PCA-SIFT feature-extraction algorithm. Finally, the mean-shift method was used to track moving objects. The experimental results showed that the optimized algorithm for tracking moving objects was significantly better and more robust than the existing algorithm. |
| first_indexed | 2024-12-16T23:28:46Z |
| format | Article |
| id | doaj.art-049fb9e9814149e497806e2d9b2c1b8f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:28:46Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-049fb9e9814149e497806e2d9b2c1b8f2022-12-21T22:11:56ZengIEEEIEEE Access2169-35362019-01-017355563556310.1109/ACCESS.2019.28993628661756Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal MinesJiang Dai-Hong0https://orcid.org/0000-0002-1163-8144Dai Lei1Li Dan2Zhang San-You3Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, Information and Electrical Engineering College, Xuzhou University of Technology, Xuzhou, ChinaKey Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, Information and Electrical Engineering College, Xuzhou University of Technology, Xuzhou, ChinaKey Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, Information and Electrical Engineering College, Xuzhou University of Technology, Xuzhou, ChinaChina University of Mining and Technology, Xuzhou, ChinaIn view of the complex and changeable environment in underground coal mines, an improved algorithm based on the principal component analysis-scale invariant feature transform (PCA-SIFT) and mean shift is proposed to address the issues for which existing tracking algorithms are not adequate; for example, when differentiating between moving targets and the background, the tracking in the case of moving objects (e.g., confusion between foreground and background) is not optimal. This results in poor resolution and the inability to deal with very dusty conditions, scale change, and rotation. The proposed feature target tracking model was developed using the scale invariance property of the PCA-SIFT feature-extraction algorithm. Finally, the mean-shift method was used to track moving objects. The experimental results showed that the optimized algorithm for tracking moving objects was significantly better and more robust than the existing algorithm.https://ieeexplore.ieee.org/document/8661756/Target trackingscale invariant feature transformmean shifttarget detection |
| spellingShingle | Jiang Dai-Hong Dai Lei Li Dan Zhang San-You Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines IEEE Access Target tracking scale invariant feature transform mean shift target detection |
| title | Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines |
| title_full | Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines |
| title_fullStr | Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines |
| title_full_unstemmed | Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines |
| title_short | Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines |
| title_sort | moving object tracking algorithm based on pca sift and optimization for underground coal mines |
| topic | Target tracking scale invariant feature transform mean shift target detection |
| url | https://ieeexplore.ieee.org/document/8661756/ |
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