@inproceedings{MalysiakRoemhildNiessetal.2017,
  author    = {Darius Malysiak and Anna-Katharina R{\"o}mhild and Christoph Nie{\"s} and Uwe Handmann},
  title     = {Boosting detection results of hog-based algorithms through non-linear metrics and roi fusion},
  series = {Asian Conference on Intelligent Information and Database Systems},
  publisher = {Springer},
  isbn      = {9783319544717},
  doi       = {DOI https://doi.org/10.1007/978-3-319-54472-4\_54},
  pages     = {577 -- 588},
  year      = {2017},
  abstract  = {Practical application of object detection systems, in research or industry, favors highly optimized black box solutions. We show how such a highly optimized system can be further augmented in terms of its reliability with only a minimal increase of computation times, i.e. preserving realtime boundaries. Our solution leaves the initial (HOG-based) detector unchanged and introduces novel concepts of non-linear metrics and fusion of ROIs. In this context we also introduce a novel way of combining feature vectors for mean-shift grouping. We evaluate our approach on a standarized image database with a HOG detector, which is representative for practical applications. Our results show that the amount of false-positive detections can be reduced by a factor of 4 with a negligable complexity increase. Although introduced and applied to a HOG-based system, our approach can easily be adapted for different detectors.},
  language  = {en}
}