Road Detection Using Similarity Search
| Authors | |
|---|---|
| Year of publication | 2011 |
| Type | Article in Proceedings |
| Conference | 2nd International Conference on Robotics in Education |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | road detection; similarity search; navigation; image classification; autonomous robot; Robotour |
| Description | This paper concerns vision-based navigation of autonomous robots. We propose a new approach for road detection based on similarity database searches. Images from the camera are divided into regular samples and for each sample the most visually similar images are retrieved from the database. The similarity between the samples and the image database is measured in a metric space using three descriptors: edge histogram, color structure and color layout, resulting in a classification of each sample into two classes: road and non-road with a confidence measure. The performance of our approach has been evaluated with respect to a manually defined ground-truth. The approach has been successfully applied to four videos consisting of more than 1180 frames. It turned out that our approach offers very precise classification results. |
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