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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ruhle, J. Rodner, E. Denzler, J. |
| Copyright Year | 2015 |
| Description | Author affiliation: Comput. Vision Group, Friedrich Schiller Univ. Jena, Jena, Germany (Ruhle, J.; Rodner, E.; Denzler, J.) |
| Abstract | Obstacle detection for advanced driver assistance systems has focused on building detectors for only a few number of object categories so far, such as pedestrians and cars. However, vulnerable obstacles of other categories are often dismissed, such as wheel-chairs and baby strollers. In our work, we try to tackle this limitation by presenting an approach which is able to predict the vulnerability of an arbitrary obstacle independently from its category. This allows for using models not specifically tuned for category recognition. To classify the vulnerability, we apply a generic category-free approach based on large random bag-of-visual-words representations (BoW), where we make use of both the intensity image as well as a given disparity map. In experimental results, we achieve a classification accuracy of over 80% for predicting one of four vulnerability levels for each of the 10000 obstacle hypotheses detected in a challenging dataset of real urban street scenes. Vulnerability prediction in general and our working algorithm in particular, pave the way to more advanced reasoning in autonomous driving, emergency route planning, as well as reducing the false-positive rate of obstacle warning systems. |
| Starting Page | 198 |
| Ending Page | 201 |
| File Size | 1014808 |
| Page Count | 4 |
| File Format | |
| ISBN | 9784901122146 |
| DOI | 10.1109/MVA.2015.7153166 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-18 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | MVA organization |
| Subject Keyword | Training Histograms Visualization Detectors Feature extraction Cameras Vehicles |
| Content Type | Text |
| Resource Type | Article |
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