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

  • Darius Malysiak
  • Anna-Katharina Römhild
  • Christoph Nieß
  • Uwe Handmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10191)

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.

Keywords

Augmentation Object detection GPGPU High performance computing Histogram of oriented gradients HOG OpenCL CUDA Meanshift grouping SVM 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Darius Malysiak
    • 1
  • Anna-Katharina Römhild
    • 2
  • Christoph Nieß
    • 1
  • Uwe Handmann
    • 1
  1. 1.Computer Science InstituteHochschule Ruhr WestMülheimGermany
  2. 2.Hochschule BochumBochumGermany

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