Wen, Zhuoman ; Kuijper, Arjan ; Fraissinet-Tachet, Matthieu ; Wang, Yanjie ; Luo, Jun (2017)
Mutual Information-Based Tracking for Multiple Cameras and Multiple Planes.
In: Arabian Journal for Science and Engineering, 42 (8)
doi: 10.1007/s13369-017-2541-z
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Based onmutual information (MI), this paper proposes a systematic analysis of tracking a multi-plane object with multiple cameras. Firstly, a geometric model consisting of a piecewise planar object and multiple cameras is setup. Given an initial pose guess, the method seeks a pose update that maximizes the global MI of all the pairs of reference image and camera image. An object pose-dependent warp is proposed to ensure computation precision. Six variations of the proposed method are designed and tested. Mode 1, i.e., computing the 2nd-order Hessian of MI at each step as the object pose changes, leads to the highest convergence rates; Mode 2, i.e., computing the 1st-order Hessian of MI once at the beginning, occupies the least time (0.5-1.0 s). For objects with simple-textured planes, applying Gaussian blur first and then useMode 1 shall generate the highest convergence rate.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Wen, Zhuoman ; Kuijper, Arjan ; Fraissinet-Tachet, Matthieu ; Wang, Yanjie ; Luo, Jun |
| Art des Eintrags: | Bibliographie |
| Titel: | Mutual Information-Based Tracking for Multiple Cameras and Multiple Planes |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Arabian Journal for Science and Engineering |
| Jahrgang/Volume einer Zeitschrift: | 42 |
| (Heft-)Nummer: | 8 |
| DOI: | 10.1007/s13369-017-2541-z |
| URL / URN: | https://link.springer.com/journal/13369/42/8/page/2 |
| Kurzbeschreibung (Abstract): | Based onmutual information (MI), this paper proposes a systematic analysis of tracking a multi-plane object with multiple cameras. Firstly, a geometric model consisting of a piecewise planar object and multiple cameras is setup. Given an initial pose guess, the method seeks a pose update that maximizes the global MI of all the pairs of reference image and camera image. An object pose-dependent warp is proposed to ensure computation precision. Six variations of the proposed method are designed and tested. Mode 1, i.e., computing the 2nd-order Hessian of MI at each step as the object pose changes, leads to the highest convergence rates; Mode 2, i.e., computing the 1st-order Hessian of MI once at the beginning, occupies the least time (0.5-1.0 s). For objects with simple-textured planes, applying Gaussian blur first and then useMode 1 shall generate the highest convergence rate. |
| Freie Schlagworte: | Computer vision, Camera tracking, Image registration, Mutual information (MI), Nonlinear optimization |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 04 Mai 2020 12:41 |
| Letzte Änderung: | 04 Mai 2020 12:41 |
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