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Image-based road type classification

Viktor Slavkovikj (UGent) , Steven Verstockt (UGent) , Wesley De Neve (UGent) , Sofie Van Hoecke (UGent) and Rik Van de Walle (UGent)
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Abstract
The ability to automatically determine the road type from sensor data is of great significance for automatic annotation of routes and autonomous navigation of robots and vehicles. In this paper, we present a novel algorithm for content-based road type classification from images. The proposed method learns discriminative features from training data in an unsupervised manner, thus not requiring domain-specific feature engineering. This is an advantage over related road surface classification algorithms which are only able to make a distinction between pre-specified uniform terrains. In order to evaluate the proposed approach, we have constructed a challenging road image dataset of 20,000 samples from real-world road images in the paved and unpaved road classes. Experimental results on this dataset show that the proposed algorithm can achieve state-of-the-art performance in road type classification.

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Citation

Please use this url to cite or link to this publication:

Chicago
Slavkovikj, Viktor, Steven Verstockt, Wesley De Neve, Sofie Van Hoecke, and Rik Van de Walle. 2014. “Image-based Road Type Classification.” In International Conference on Pattern Recognition, 2359–2364.
APA
Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., & Van de Walle, R. (2014). Image-based road type classification. International Conference on Pattern Recognition (pp. 2359–2364). Presented at the 22nd International Conference on Pattern Recognition (ICPR).
Vancouver
1.
Slavkovikj V, Verstockt S, De Neve W, Van Hoecke S, Van de Walle R. Image-based road type classification. International Conference on Pattern Recognition. 2014. p. 2359–64.
MLA
Slavkovikj, Viktor, Steven Verstockt, Wesley De Neve, et al. “Image-based Road Type Classification.” International Conference on Pattern Recognition. 2014. 2359–2364. Print.
@inproceedings{5821871,
  abstract     = {The ability to automatically determine the road type from sensor data is of great significance for automatic annotation of routes and autonomous navigation of robots and vehicles. In this paper, we present a novel algorithm for content-based road type classification from images. The proposed method learns discriminative features from training data in an unsupervised manner, thus not requiring domain-specific feature engineering. This is an advantage over related road surface classification algorithms which are only able to make a distinction between pre-specified uniform terrains. In order to evaluate the proposed approach, we have constructed a challenging road image dataset of 20,000 samples from real-world road images in the paved and unpaved road classes. Experimental results on this dataset show that the proposed algorithm can achieve state-of-the-art performance in road type classification.},
  author       = {Slavkovikj, Viktor and Verstockt, Steven and De Neve, Wesley and Van Hoecke, Sofie and Van de Walle, Rik},
  booktitle    = {International Conference on Pattern Recognition},
  isbn         = {978-1-4799-5208-3},
  issn         = {1051-4651},
  language     = {eng},
  location     = {Stockholm, Sweden},
  pages        = {2359--2364},
  title        = {Image-based road type classification},
  year         = {2014},
}

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