Multi-Task Deep Dual Correlation Filters for Visual Tracking
Correlation filters combined with deep features have delivered impressive results in visual tracking task. However, existing approaches treat deep features produced by different network layers independently, limiting their representation power. To address this issue, this paper proposes a multi-task...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - PP(2020) vom: 15. Okt. |
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| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Correlation filters combined with deep features have delivered impressive results in visual tracking task. However, existing approaches treat deep features produced by different network layers independently, limiting their representation power. To address this issue, this paper proposes a multi-task deep dual correlation filters (MDDCF) based method for robust visual tracking. First, a new multi-task learning scheme is designed to take full advantage of the multi-level features of deep networks, where target representation with individual features is regarded as a single task. As such, the interdependencies between different levels of features can be better explored. Second, we reformulate the objective function of the dual correlation filters and propose a new alternating optimization method, allowing joint training of the correlation filters and network parameters. Third, we design an effective object template update scheme which can well capture the target appearance variations. Extensive experimental evaluations on seven benchmark datasets show that the proposed MDDCF tracker performs favorably against state-ofthe-art methods |
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| Beschreibung: | Date Revised 22.02.2024 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2020.3029897 |