Visual tracking using locality‐constrained linear coding under a particle filtering framework
Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on loc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-03-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2017.0271 |
| _version_ | 1797684930647425024 |
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| author | Meng Ding Li Wei Yunfeng Cao Jie Wang Li Cao |
| author_facet | Meng Ding Li Wei Yunfeng Cao Jie Wang Li Cao |
| author_sort | Meng Ding |
| collection | DOAJ |
| description | Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on locality‐constrained linear coding (LLC) under a particle filtering framework. A local feature descriptor is presented that can evenly represent the local information of each patch in the tracking region. LLC uses the locality constraints to project each local feature descriptor into its local‐coordinate system. Compared with sparse coding, LLC can be performed very quickly for appearance modelling because it has an analytical solution derived by a three‐step matrix calculation, and the computational complexity of the proposed tracking algorithm is o(η×m×n). Both quantitative and qualitative experimental results demonstrate that the authors’ proposed algorithm performs favourably against the 10 state‐of‐the‐art trackers on 12 challenging test sequences. However, related experimental results show that the performance of their tracker is not effective enough for small tracking targets owing to a lack of sufficient local region information. |
| first_indexed | 2024-03-12T00:36:56Z |
| format | Article |
| id | doaj.art-25a376a29604439ba10a6e3e0f18253d |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:36:56Z |
| publishDate | 2018-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-25a376a29604439ba10a6e3e0f18253d2023-09-15T09:32:39ZengWileyIET Computer Vision1751-96321751-96402018-03-0112219620710.1049/iet-cvi.2017.0271Visual tracking using locality‐constrained linear coding under a particle filtering frameworkMeng Ding0Li Wei1Yunfeng Cao2Jie Wang3Li Cao4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics29 Jiangjun StreetNanjingPeople's Republic of ChinaJin Cheng College, Nanjing University of Aeronautics and Astronautics88 Hangjin StreetNanjingPeople's Republic of ChinaCollege of Astronautics, Nanjing University of Aeronautics and Astronautics29 Yudao StreetNanjingPeople's Republic of ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics29 Jiangjun StreetNanjingPeople's Republic of ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics29 Jiangjun StreetNanjingPeople's Republic of ChinaVisual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on locality‐constrained linear coding (LLC) under a particle filtering framework. A local feature descriptor is presented that can evenly represent the local information of each patch in the tracking region. LLC uses the locality constraints to project each local feature descriptor into its local‐coordinate system. Compared with sparse coding, LLC can be performed very quickly for appearance modelling because it has an analytical solution derived by a three‐step matrix calculation, and the computational complexity of the proposed tracking algorithm is o(η×m×n). Both quantitative and qualitative experimental results demonstrate that the authors’ proposed algorithm performs favourably against the 10 state‐of‐the‐art trackers on 12 challenging test sequences. However, related experimental results show that the performance of their tracker is not effective enough for small tracking targets owing to a lack of sufficient local region information.https://doi.org/10.1049/iet-cvi.2017.0271local region informationcomputational complexitymatrix calculationlocal feature descriptorLLCtracking algorithm |
| spellingShingle | Meng Ding Li Wei Yunfeng Cao Jie Wang Li Cao Visual tracking using locality‐constrained linear coding under a particle filtering framework IET Computer Vision local region information computational complexity matrix calculation local feature descriptor LLC tracking algorithm |
| title | Visual tracking using locality‐constrained linear coding under a particle filtering framework |
| title_full | Visual tracking using locality‐constrained linear coding under a particle filtering framework |
| title_fullStr | Visual tracking using locality‐constrained linear coding under a particle filtering framework |
| title_full_unstemmed | Visual tracking using locality‐constrained linear coding under a particle filtering framework |
| title_short | Visual tracking using locality‐constrained linear coding under a particle filtering framework |
| title_sort | visual tracking using locality constrained linear coding under a particle filtering framework |
| topic | local region information computational complexity matrix calculation local feature descriptor LLC tracking algorithm |
| url | https://doi.org/10.1049/iet-cvi.2017.0271 |
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