Pedestrian Detection Algorithms using Shearlets

Pedestrian Detection Algorithms using Shearlets

In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this t...

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Bibliographische Detailangaben
1. Verfasser: Pfeifer, Lienhard
Beteiligte: Dahlke, Stephan (Prof. Dr.) (BetreuerIn (Doktorarbeit))
Format: Dissertation
Sprache:Englisch
Veröffentlicht: Philipps-Universität Marburg 2018
Schlagworte:
Shearlets
Machine Learning
Pedestrian Detection
Convolutional Neural Net
Mathematik
Computer Vision
Bildverarbeitung
Fußgängererkennung
Deep Learning
Artificial Intelligence
Künstliche Intelligenz
Image Processing
Mathematics
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Beschreibung
Zusammenfassung:In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this time period, a wealth of different algorithms has been developed. According to the current results on the Caltech Pedestrian Detection Benchmark the algorithms can be divided into two categories. First, application of hand-crafted image features and of a classifier trained on these features. Second, methods using Convolutional Neural Networks in which features are learned during the training phase. It is studied how both of these types of procedures can be further improved by the incorporation of shearlets, a framework for image analysis which has a comprehensive theoretical basis.
Umfang:181 Seiten
DOI:10.17192/z2018.0523

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