Pothole Detection and Tracking in Car Video Sequence
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | International Conference on Telecommunications and Signal Processing (TSP) |
| Kustantaja | IEEE |
| Sivut | 701-706 |
| Sivumäärä | 6 |
| ISBN (elektroninen) | 978-1-5090-1288-6 |
| ISBN (painettu) | 978-1-5090-1287-9 |
| Tila | Julkaistu - kesäkuuta 2016 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | International Conference on Telecommunications and Signal Processing - , Iso-Britannia Kesto: 1 tammikuuta 2015 → … |
Conference
| Conference | International Conference on Telecommunications and Signal Processing |
|---|---|
| Maa | Iso-Britannia |
| Ajanjakso | 1/01/15 → … |
Tiivistelmä
In this paper, we propose a low complexity method for detection and tracking of potholes in video sequences taken by a camera placed inside a moving car.
The region of interest for the detection of the potholes is selected as the image area where the road is observed with the highest resolution. A threshold-based algorithm generates a set of candidate regions.
For each region the following features are extracted: its size, the regularity of the intensity surface, contrast with respect to background model, and the region's contour length and shape. The candidate regions are labeled as putative potholes by a decision tree according to these features, eliminating the false positives due to shadows of wayside objects.
The putative potholes that are successfully tracked in consecutive frames are finally declared potholes.
Experimental results with real video sequences show a good detection precision.
The region of interest for the detection of the potholes is selected as the image area where the road is observed with the highest resolution. A threshold-based algorithm generates a set of candidate regions.
For each region the following features are extracted: its size, the regularity of the intensity surface, contrast with respect to background model, and the region's contour length and shape. The candidate regions are labeled as putative potholes by a decision tree according to these features, eliminating the false positives due to shadows of wayside objects.
The putative potholes that are successfully tracked in consecutive frames are finally declared potholes.
Experimental results with real video sequences show a good detection precision.
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