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.
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): | No Creative Commons |