Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching
Although multi‐objects tracking has been improved significantly, tracking multiple aircrafts with nearly the same appearance remains a difficult task, especially when a significant pose changes and long‐time occlusions occur in the complex environment. In this study, the authors propose a new multi‐...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-12-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2014.0403 |
| _version_ | 1797684956717121536 |
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| author | Zehua Xie Zhenzhong Wei Chen Bai |
| author_facet | Zehua Xie Zhenzhong Wei Chen Bai |
| author_sort | Zehua Xie |
| collection | DOAJ |
| description | Although multi‐objects tracking has been improved significantly, tracking multiple aircrafts with nearly the same appearance remains a difficult task, especially when a significant pose changes and long‐time occlusions occur in the complex environment. In this study, the authors propose a new multi‐aircrafts tracker based on a structured support vector machine (SVM) and an intra‐frame scale‐invariant feature transform feature matching. The structured SVM‐based model adapts to the appearance change well, but confuses different aircrafts when occlusions between aircrafts occur. To handle occlusions, an intra‐frame matching method is applied to separate different aircrafts by matching points into different clusters. Moreover, to remove the mismatching caused by the cluttered background, the spatial–temporal constraint is applied to help improve the performance of the intra‐frame feature matching. As there is no dataset to evaluate a multi‐aircrafts tracker, they select eighteen challenging videos and manually annotate the ground truth, forming the first multi‐aircrafts tracking dataset. The experiments in the dataset demonstrate that the author's tracker outperforms the state‐of‐the‐art trackers in multi‐aircrafts tracking. |
| first_indexed | 2024-03-12T00:37:23Z |
| format | Article |
| id | doaj.art-a2b5cb1c3f5e46b39f567df06f5515cf |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:37:23Z |
| publishDate | 2015-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-a2b5cb1c3f5e46b39f567df06f5515cf2023-09-15T09:29:27ZengWileyIET Computer Vision1751-96321751-96402015-12-019683184010.1049/iet-cvi.2014.0403Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matchingZehua Xie0Zhenzhong Wei1Chen Bai2Key Laboratory of Precision Opto‐Mechatronics TechnologyMinistry of Education Beihang UniversityBeijingPeople's Republic of ChinaKey Laboratory of Precision Opto‐Mechatronics TechnologyMinistry of Education Beihang UniversityBeijingPeople's Republic of ChinaKey Laboratory of Precision Opto‐Mechatronics TechnologyMinistry of Education Beihang UniversityBeijingPeople's Republic of ChinaAlthough multi‐objects tracking has been improved significantly, tracking multiple aircrafts with nearly the same appearance remains a difficult task, especially when a significant pose changes and long‐time occlusions occur in the complex environment. In this study, the authors propose a new multi‐aircrafts tracker based on a structured support vector machine (SVM) and an intra‐frame scale‐invariant feature transform feature matching. The structured SVM‐based model adapts to the appearance change well, but confuses different aircrafts when occlusions between aircrafts occur. To handle occlusions, an intra‐frame matching method is applied to separate different aircrafts by matching points into different clusters. Moreover, to remove the mismatching caused by the cluttered background, the spatial–temporal constraint is applied to help improve the performance of the intra‐frame feature matching. As there is no dataset to evaluate a multi‐aircrafts tracker, they select eighteen challenging videos and manually annotate the ground truth, forming the first multi‐aircrafts tracking dataset. The experiments in the dataset demonstrate that the author's tracker outperforms the state‐of‐the‐art trackers in multi‐aircrafts tracking.https://doi.org/10.1049/iet-cvi.2014.0403spatial-temporal constraintintraframe scale-invariant feature transform feature matchingmultiobject trackingmultiple aircraft trackingocclusionstructured support vector machine |
| spellingShingle | Zehua Xie Zhenzhong Wei Chen Bai Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching IET Computer Vision spatial-temporal constraint intraframe scale-invariant feature transform feature matching multiobject tracking multiple aircraft tracking occlusion structured support vector machine |
| title | Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching |
| title_full | Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching |
| title_fullStr | Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching |
| title_full_unstemmed | Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching |
| title_short | Multi‐aircrafts tracking using spatial–temporal constraints‐based intra‐frame scale‐invariant feature transform feature matching |
| title_sort | multi aircrafts tracking using spatial temporal constraints based intra frame scale invariant feature transform feature matching |
| topic | spatial-temporal constraint intraframe scale-invariant feature transform feature matching multiobject tracking multiple aircraft tracking occlusion structured support vector machine |
| url | https://doi.org/10.1049/iet-cvi.2014.0403 |
| work_keys_str_mv | AT zehuaxie multiaircraftstrackingusingspatialtemporalconstraintsbasedintraframescaleinvariantfeaturetransformfeaturematching AT zhenzhongwei multiaircraftstrackingusingspatialtemporalconstraintsbasedintraframescaleinvariantfeaturetransformfeaturematching AT chenbai multiaircraftstrackingusingspatialtemporalconstraintsbasedintraframescaleinvariantfeaturetransformfeaturematching |