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Boosting detection results of hog-based algorithms through non-linear metrics and roi fusion

  • 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.

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Metadaten
Author:Darius Malysiak, Anna-Katharina Römhild, Christoph Nieß, Uwe Handmann
DOI:https://doi.org/DOI https://doi.org/10.1007/978-3-319-54472-4_54
ISBN:9783319544717
Parent Title (English):Asian Conference on Intelligent Information and Database Systems
Publisher:Springer
Document Type:Conference Proceeding
Language:English
Year of Completion:2017
Release Date:2019/04/30
First Page:577
Last Page:588
Institutes:Fachbereich 1 - Institut Informatik
DDC class:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):License LogoNo Creative Commons