Zhou, Tong-xue ; Zeng, Dong-dong ; Zhu, Ming ; Kuijper, Arjan (2019)
A template consensus method for visual tracking.
In: Optoelectronics Letters, 15 (1)
doi: 10.1007/s11801-019-8109-2
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Abstract: Visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreground detection, which classifies a given pixel as foreground or background based on its similarity to recently observed samples, we present a template consensus tracker based on the kernelized correlation filter (KCF). Instead of keeping only one target appearance model in the KCF, we make a feature pool to keep several target appearance models in our method and predict the new target position by searching for the location of the maximal value of the response maps. Both quantitative and qualitative evaluations are performed on the CVPR2013 tracking benchmark dataset. The results show that our proposed method improves the original KCF tracker by 8.17% in the success plot and 8.11% in the precision plot.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Zhou, Tong-xue ; Zeng, Dong-dong ; Zhu, Ming ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | A template consensus method for visual tracking |
| Sprache: | Englisch |
| Publikationsjahr: | 2019 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Optoelectronics Letters |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1007/s11801-019-8109-2 |
| URL / URN: | https://doi.org/10.1007/s11801-019-8109-2 |
| Kurzbeschreibung (Abstract): | Abstract: Visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreground detection, which classifies a given pixel as foreground or background based on its similarity to recently observed samples, we present a template consensus tracker based on the kernelized correlation filter (KCF). Instead of keeping only one target appearance model in the KCF, we make a feature pool to keep several target appearance models in our method and predict the new target position by searching for the location of the maximal value of the response maps. Both quantitative and qualitative evaluations are performed on the CVPR2013 tracking benchmark dataset. The results show that our proposed method improves the original KCF tracker by 8.17% in the success plot and 8.11% in the precision plot. |
| Freie Schlagworte: | Computer vision based tracking, Object tracking, Tracking, Filtering, Appearance |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 19 Jun 2019 11:16 |
| Letzte Änderung: | 19 Jun 2019 11:16 |
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