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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Santos, M. Linder, M. Schnitman, L. Nunes, U. Oliveira, L. |
| Copyright Year | 2013 |
| Description | Author affiliation: Inst. of Syst. & Robot., Coimbra Univ., Coimbra, Portugal (Nunes, U.) || Intell. Vision Res. Lab., Fed. Univ. of Bahia, Salvador, Brazil (Santos, M.; Linder, M.; Schnitman, L.; Oliveira, L.) |
| Abstract | Road segmentation plays an important role in many computer vision applications, either for in-vehicle perception or traffic surveillance. In camera-equipped vehicles, road detection methods are being developed for advanced driver assistance, lane departure, and aerial incident detection, just to cite a few. In traffic surveillance, segmenting road information brings special benefits: to automatically wrap regions of traffic analysis (consequently, speeding up flow analysis in videos), to help with the detection of driving violations (to improve contextual information in videos of traffic), and so forth. Methods and techniques can be used interchangeably for both types of application. Particularly, we are interested in segmenting road regions from the remaining of an image, aiming to support traffic flow analysis tasks. In our proposed method, road segmentation relies on a superpixel detection based on a novel edge density estimation method; in each superpixel, priors are extracted from features of gray-amount, texture homogeneity, traffic motion and horizon line. A feature vector with all those priors feeds a support vector machine classifier, which ultimately takes the superpixel-wise decision of being a road or not. A dataset of challenging scenes was gathered from traffic video surveillance cameras, in our city, to demonstrate the effectiveness of the method. |
| Sponsorship | IEEE Intell. Transp. Syst. Soc. |
| Starting Page | 527 |
| Ending Page | 532 |
| File Size | 3006422 |
| Page Count | 6 |
| File Format | |
| ISSN | 19310587 |
| e-ISBN | 9781467327558 |
| DOI | 10.1109/IVS.2013.6629521 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-23 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Roads Image edge detection Feature extraction Vehicles Image segmentation Motion segmentation Detectors |
| Content Type | Text |
| Resource Type | Article |
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