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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Boosting Detection Results of HOG-Based Algorithms Through Non-linear Metrics and ROI Fusion
verfasst von : Darius Malysiak, Anna-Katharina Römhild, Christoph Nieß, Uwe Handmann
Erschienen in: Intelligent Information and Database Systems
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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.