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Analysis of Regionlets for Pedestrian Detection

Salscheider, Niels Ole; Rehder, Eike; Lauer, Martin

Abstract:
Human detection is an important task for many autonomous robots as well as automated driving systems. The Regionlets detector was one of the best-performing approaches for pedestrian detection on the KITTI dataset during 2015. We analysed the Regionlets detector and its performance. This paper discusses the improvements in accuracy that were achieved by the different ideas of the Regionlets detector. It also analyses what the boosting algorithm learns and how this relates to the expectations. We found that the random generation of regionlet configurations can be replaced by a regular grid of regionlets. Doing so reduces the dimensionality of the feature space drastically but does not decrease detection performance. This translates into a decrease in memory consumption and computing time during training.


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Originalveröffentlichung
DOI: 10.5220/0006094100260032
Zugehörige Institution(en) am KIT Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
Karlsruhe School of Optics & Photonics (KSOP)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2017
Sprache Englisch
Identifikator ISBN: 978-9897582226
KITopen-ID: 1000134192
Erschienen in Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods: 24-26 February 2017 ; Porto, Portugal
Veranstaltung 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), Porto, Portugal, 24.02.2017 – 26.02.2017
Verlag SciTePress
Seiten 26–32
Nachgewiesen in Scopus
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