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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Robust Fiducal Marker Detection with Fully Convolutional Neural Network |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 42 |
| Date | 2019 |
| Abstract Text | Fiducial marker systems offer an alternative means of camera pose estimation to keypoint-based methods. Modern systems such as Aruco work with squareshaped markers with an external, black border comprising a black-and-white bit pattern. Their articial appearance makes them easy to spot in many real world environments. Nevertheless, state-of-the-art methods still perform poorly under challenging conditions such as motion blur, dicult view angles, small scale or non-uniform lighting. We propose a new detection system based on fully convolutional neural networks trained on synthetic data. By introducing several visual and spatial transformations to the synthetic markers we aim to add more robustness than current detection systems. With synthetic and real world experiments we show that our method is in fact able to detect more markers from a greater distance, distored with motion blur or under diffcult lighting conditions. |
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