In the last couple of years, the terms Virtual Reality and Augmented Reality increased in importance. For most of the provided functionality a basic VR/AR-device is enough. On the other hand, there are some areas of application where additional technology is mandatory. An example, where additional technology is needed, is the training of emergency drivers. For this scenario and exact position of the windscreen is needed. So that, additional hazards can be displayed. This thesis is focusing on this problem and tries to show the possibilities with edge detection algorithms to detect windscreens in vehicles. Therefore, there are two central questions. “It is possible to detect windows purely by means of edge detection” and “Which are the most common algorithms of edge detection and what are their main advantages and disadvantages”. To answer these two questions, this thesis is split into two parts. On the one hand, there is a theoretical part, and on the other hand, there are elaborations of prototypes. The theoretical part, describes the basic functionality of different edge detection algorithms. As well as it checks the possibility to use it for detecting windows. Essentially, the following operators / procedures were considered: Roberts, Prewitt, Sobel, Kompass-Gradient, Kirsch, Marr-Hildreth und Canny. Three prototype were developed, to compare the algorithms in its functionality. The selected ones are Marr-Hildreth and Canny, because they can be parameterized and therefore they are quite well for this scenario. The third one is Prewitt, which is used to compare it to the non-parameterized ones. With the results of the prototypes, it is now able to check which of them solves which scenario best. It also describes, which settings and parameters are needed to achieve this result. For the prototypes following algorithms were used: Prewitt, Marr-Hildreth und Canny. The achieved results of the prototypes were very different. In some scenarios, the edge detection algorithms had trouble to deal with the scenario. Whereas in some scenarios the results were good. Generally, the prototypes showed the typical problems with edge detection algorithms. Disturbing factors such as rushing or blurring were a common problem. The use of edge detection algorithms to detect windows is possible for some standard scenarios. Especially when there are very few interfering factors. To enable the detection in other scenarios, additional procedures are required. An example therefore would be object detection.
|Translated title of the contribution||Edge detection for window recognition in vehicle|
|Number of pages||46|
|Publication status||Published - 2017|