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Boosting Detection Results of HOG-Based Algorithms Through Non-linear Metrics and ROI Fusion

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

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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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Correspondence to Darius Malysiak .

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Malysiak, D., Römhild, AK., Nieß, C., Handmann, U. (2017). Boosting Detection Results of HOG-Based Algorithms Through Non-linear Metrics and ROI Fusion. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham.

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  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

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