Object Tracking With Structured Metric Learning
In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning. In our method, tracking is formulated as a structured metric learning problem, which not only considers the importance of differ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8888253/ |
| _version_ | 1819174243533848576 |
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| author | Xiaolin Zhao Zhuofan Xu Boxin Zhao Xiaolong Chen Zongzhe Li |
| author_facet | Xiaolin Zhao Zhuofan Xu Boxin Zhao Xiaolong Chen Zongzhe Li |
| author_sort | Xiaolin Zhao |
| collection | DOAJ |
| description | In this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning. In our method, tracking is formulated as a structured metric learning problem, which not only considers the importance of different samples, but also improves the discriminability by learning a specific distance metric for matching. Specifically, a concrete structured metric learning method is realized by making use of the constraints from the target and its neighbour training samples under the above framework. Besides, a closed-form solution is derived for the structured metric learning problem. To improve the matching robustness, the K-nearest neighbours (KNN) distance is employed to determine the final tracking result. Experimental results in the benchmark dataset demonstrate that the proposed structured metric learning based tracking method can achieve desirable performance. |
| first_indexed | 2024-12-22T20:35:53Z |
| format | Article |
| id | doaj.art-52b97281c3174fcfb49817589925203b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:35:53Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-52b97281c3174fcfb49817589925203b2022-12-21T18:13:27ZengIEEEIEEE Access2169-35362019-01-01716176416177510.1109/ACCESS.2019.29506908888253Object Tracking With Structured Metric LearningXiaolin Zhao0Zhuofan Xu1https://orcid.org/0000-0003-2603-907XBoxin Zhao2Xiaolong Chen3Zongzhe Li4Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, ChinaJoint Operations College, National Defense University, Shijiazhuang, ChinaEquipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, ChinaFlight Automatic Control Research Institute, Xi’an, ChinaEquipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, ChinaIn this paper, we propose a novel tracking method based on structured metric learning, which takes the advantages of both structured learning and distance metric learning. In our method, tracking is formulated as a structured metric learning problem, which not only considers the importance of different samples, but also improves the discriminability by learning a specific distance metric for matching. Specifically, a concrete structured metric learning method is realized by making use of the constraints from the target and its neighbour training samples under the above framework. Besides, a closed-form solution is derived for the structured metric learning problem. To improve the matching robustness, the K-nearest neighbours (KNN) distance is employed to determine the final tracking result. Experimental results in the benchmark dataset demonstrate that the proposed structured metric learning based tracking method can achieve desirable performance.https://ieeexplore.ieee.org/document/8888253/Object trackingstructured metric learningKNN distance |
| spellingShingle | Xiaolin Zhao Zhuofan Xu Boxin Zhao Xiaolong Chen Zongzhe Li Object Tracking With Structured Metric Learning IEEE Access Object tracking structured metric learning KNN distance |
| title | Object Tracking With Structured Metric Learning |
| title_full | Object Tracking With Structured Metric Learning |
| title_fullStr | Object Tracking With Structured Metric Learning |
| title_full_unstemmed | Object Tracking With Structured Metric Learning |
| title_short | Object Tracking With Structured Metric Learning |
| title_sort | object tracking with structured metric learning |
| topic | Object tracking structured metric learning KNN distance |
| url | https://ieeexplore.ieee.org/document/8888253/ |
| work_keys_str_mv | AT xiaolinzhao objecttrackingwithstructuredmetriclearning AT zhuofanxu objecttrackingwithstructuredmetriclearning AT boxinzhao objecttrackingwithstructuredmetriclearning AT xiaolongchen objecttrackingwithstructuredmetriclearning AT zongzheli objecttrackingwithstructuredmetriclearning |