L
Luc Van Gool
Researcher at Katholieke Universiteit Leuven
Publications - 1458
Citations - 137230
Luc Van Gool is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 133, co-authored 1307 publications receiving 107743 citations. Previous affiliations of Luc Van Gool include Microsoft & ETH Zurich.
Papers
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Journal Article
Articulated multi-body tracking under egomotion
TL;DR: In this article, the authors address the problem of 3D articulated multi-person tracking in busy street scenes from a moving, human-level observer and propose a two-stage strategy.
Book ChapterDOI
ViRoom - Low Cost Synchronized Multicamera System and Its Self-calibration
TL;DR: This paper presents a multicamera Visual Room (ViRoom), constructed from low-cost digital cameras and standard computers running on Linux and a fully automatic self-calibration method for multiple cameras and without any known calibration object.
Book ChapterDOI
A Compact Model for Viewpoint Dependent Texture Synthesis
Alexey Zalesny,Luc Van Gool +1 more
TL;DR: In this paper, a texture synthesis method is presented that generates similar texture from an example image, based on the emulation of simple but rather carefully chosen image intensity statistics, and the resulting texture models are compact and no longer require the example image from which they were derived.
Proceedings ArticleDOI
A distributed camera system for multi-resolution surveillance
Nicola Bellotto,Eric Sommerlade,Ben Benfold,Charles Bibby,Ian Reid,Daniel Roth,Carles Fernández,Luc Van Gool,Jordi Gonzàlez +8 more
TL;DR: An architecture for a multi-camera, multi-resolution surveillance system to support a set of distributed static and pan-tilt-zoom cameras and visual tracking algorithms, together with a central supervisor unit is described.
Proceedings ArticleDOI
Fast Algorithms for Linear and Kernel SVM
TL;DR: This paper proposes two efficient algorithms for solving the linear and kernel SVM+, and shows that their new dual problem can be efficiently solved by using the SMO algorithm of the one-class SVM problem.