TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Malysiak, Darius
A1  - Römhild, Anna-Katharina
A1  - Nieß, Christoph
A1  - Handmann, Uwe
T1  - Boosting detection results of hog-based algorithms through non-linear metrics and roi fusion
T2  - Asian Conference on Intelligent Information and Database Systems
N2  - 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.
Y1  - 2017
SN  - 9783319544717
SB  - 9783319544717
U6  - https://doi.org/DOI https://doi.org/10.1007/978-3-319-54472-4_54
DO  - https://doi.org/DOI https://doi.org/10.1007/978-3-319-54472-4_54
SP  - 577
EP  - 588
PB  - Springer
ER  -