@techreport{KurzHaentschGrosseetal.2007,
  author    = {Kurz, Daniel and H{\"a}ntsch, Ferry and Grosse, Max and Schiewe, Alexander and Bimber, Oliver},
  title     = {Laser Pointer Tracking in Projector-Augmented Architectural Environments},
  doi       = {10.25643/bauhaus-universitaet.818},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-8183},
  year      = {2007},
  abstract  = {We present a system that applies a custom-built pan-tilt-zoom camera for laser-pointer tracking in arbitrary real environments. Once placed in a building environment, it carries out a fully automatic self-registration, registrations of projectors, and sampling of surface parameters, such as geometry and reflectivity. After these steps, it can be used for tracking a laser spot on the surface as well as an LED marker in 3D space, using inter-playing fisheye context and controllable detail cameras. The captured surface information can be used for masking out areas that are critical to laser-pointer tracking, and for guiding geometric and radiometric image correction techniques that enable a projector-based augmentation on arbitrary surfaces. We describe a distributed software framework that couples laser-pointer tracking for interaction, projector-based AR as well as video see-through AR for visualizations with the domain specific functionality of existing desktop tools for architectural planning, simulation and building surveying.},
  subject      = {Association for Computing Machinery / Special Interest Group on Graphics},
  language  = {en}
}
@techreport{ExnerBrunsKurzetal.2009,
  author    = {Exner, David and Bruns, Erich and Kurz, Daniel and Grundh{\"o}fer, Anselm and Bimber, Oliver},
  title     = {Fast and Reliable CAMShift Tracking},
  organization    = {JP AUgmented Reality, Bauhaus-Universit{\"a}t Weimar},
  doi       = {10.25643/bauhaus-universitaet.1410},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20091217-14962},
  year      = {2009},
  abstract  = {CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object's appearance changes (e.g. due to object or camera movement, or due to lighting changes), when similarly colored objects have to be re-detected or when they cross their trajectories. We propose extensions to CAMShift that address and resolve all of these problems. They allow the accumulation of multiple histograms to model more complex object appearance and the continuous monitoring of object identi- ties to handle ambiguous cases of partial or full occlusion. Most steps of our method are carried out on the GPU for achieving real-time tracking of multiple targets simultaneously. We explain an ecient GPU implementations of histogram generation, probability back projection, im- age moments computations, and histogram intersection. All of these techniques make full use of a GPU's high parallelization.},
  subject      = {Bildverarbeitung},
  language  = {en}
}