2013 IEEE Intelligent Vehicles Symposium (IV) 2013
DOI: 10.1109/ivs.2013.6629589
|View full text |Cite
|
Sign up to set email alerts
|

A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments

Abstract: This paper presents an approach for road detection based on image segmentation. This segmentation is resulted from merging 2D and 3D image processing data from a stereo vision system. The 2D layer returns a matrix containing pixel's clusters based on the Watershed transform. Whereas the 3D layer return labels, that are classified by the V-Disparity technique, to free spaces, obstacles and non-classified area. Thus, a feature's descriptor for each cluster is composed with features from both layers. The road pat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
26
0

Year Published

2013
2013
2023
2023

Publication Types

Select...
4
3

Relationship

2
5

Authors

Journals

citations
Cited by 30 publications
(26 citation statements)
references
References 24 publications
0
26
0
Order By: Relevance
“…The time to train each category takes around 24 hours and the classification time for a single image takes around 3 seconds. It should be mentioned that some adaptations from the first version proposed in [11] were required: (i) it does not use the moving average technique, because it is applicable only for image sequences, (ii) the learning process does not use the strategy of training with subclass (shadow area, normal area and land marks), where it would improve the final result of road detection. In the case of the learning process to Joint Boosting algorithm, the training images were splitted into training subset containing 40%, and the test subset with 60%.…”
Section: Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…The time to train each category takes around 24 hours and the classification time for a single image takes around 3 seconds. It should be mentioned that some adaptations from the first version proposed in [11] were required: (i) it does not use the moving average technique, because it is applicable only for image sequences, (ii) the learning process does not use the strategy of training with subclass (shadow area, normal area and land marks), where it would improve the final result of road detection. In the case of the learning process to Joint Boosting algorithm, the training images were splitted into training subset containing 40%, and the test subset with 60%.…”
Section: Resultsmentioning
confidence: 99%
“…The second row presents the ground truth, followed by the next two rows that are the HistonBoost result and Artificial Neural Network result. complexity of the Kitti-road dataset, or, the training process used for ANN was not adequate, having low expressiveness if observed the strategy of subclasses used in [11]. To conclude the evaluation process, Table IV presents the final results merging all categories.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…For another hand, road detection methods can also be categorized into feature-based [15,18,19,42], [16] 2008 Laser range finder, HDL 64E Lidar, binocular camera system Urban Junior 3 [17] 2010 Laser range finder,Riegl laser, HDL 64E Lidar, Bosch Radae, colored camera Urban VRC [36] 2013 Laser range finder, binocular camera system Complex environment model-based [25,44,46] and region-based [11,19]. Feature-based method extract road features such as color, texture, boundary, etc.…”
Section: Related Workmentioning
confidence: 99%