SC Seminar: Daniel Maue

Daniel Maue, RPTU Kaiserslautern-Landau

Title: Depth-Aware Traffic Sign Recognition – A Multi-Stage 3D Localization & Classification Pipeline for Traffic Signs


Recognizing objects within images is a key task in computer vision. There are many traditional algorithmic approaches for locating objects in images and with the rise of research of artificial intelligence also deep learning recognition models were proposed. State-of-the-art methods already achieve matching or better performance than humans. The goal of this thesis is to give the common recognition task a third dimension, by not only locating objects in the 2D image but also estimating the depth of this object within the captured scene. Especially traffic sign recognition would benefit from the additional depth information, since capturing the distance and thus the order of oncoming traffic signs could greatly improve safety in vehicles since the depth can have important semantic meanings to the current and future driver assistance system. Therefore this bachelor thesis focuses on proposing a multi-stage 3D localization pipeline for traffic signs, by thoroughly analyzing and comparing state-of-the-art methods in object recognition and depth estimation. With that, these two topics are first introduced by covering related work and the needed theoretical background. Additionally, the considered approaches for object recognition and depth estimation are explained and finally evaluated in experiments, to find the most suitable methods for a 3D localization pipeline.

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