@inproceedings{6073,
  title = {Active Structured Learning for High-Speed Object Detection},
  journal = {Pattern Recognition: 31st DAGM Symposium},
  booktitle = {DAGM 2009},
  abstract = {High-speed smooth and accurate visual tracking of objects in
  arbitrary, unstructured environments is essential for robotics and human
  motion analysis. However, building a system that can adapt to arbitrary
  objects and a wide range of lighting conditions is a challenging problem,
  especially if hard real-time constraints apply like in robotics scenarios.
  In this work, we introduce a method for learning a discriminative object
  tracking system based on the recent structured regression framework for
  object localization. Using a kernel function that allows fast evaluation
  on the GPU, the resulting system can process video streams at speed of
  100 frames per second or more.
  Consecutive frames in high speed video sequences are typically very redundant,
  and for training an object detection system, it is sufficient to
  have training labels from only a subset of all images. We propose an
  active learning method that select training examples in a data-driven
  way, thereby minimizing the required number of training labeling. Experiments
  on realistic data show that the active learning is superior to
  previously used methods for dataset subsampling for this task.},
  pages = {221-231},
  editors = {Denzler, J. , G. Notni, H. S{\"u}sse},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2009},
  author = {Lampert, CH. and Peters, J.},
  doi = {10.1007/978-3-642-03798-6_23},
  month_numeric = {9}
}