Download document 8073216

2010 3rd International Conference on Computer and Electrical Engineering
(ICCEE 2010)
IPCSIT vol. 53 (2012) © (2012) IACSIT Press, Singapore
DOI: 10.7763/IPCSIT.2012.V53.No.1.21
Road Segmentation Based on Learning Classification
Yiming Nie+, Zhenping Sun, Daxue Liu, Tao Wu and Bin Dai
College of Mechatronical Engineering and Automation
National University of Defense Technology
Changsha 410073, China
Abstract. In vision navigation tasks, the road segmentation is a useful method. Usually, roads can be
detected using image segmentation and related image process methods. However such methods always rely
on specific road prior knowledge, and they are difficult to be realized in different environments. In this paper
the learning classification is proposed. In the learning process the roads of different environments are labeled
and learned. In the classification process the road is segmented and selected. Experiments results show that
even the roads change the correct results could be got for online process.
Keywords: Monocular vision, image segment, super pixel, road detection
1. Introduction and Related Works
Autonomous Guided Vehicles through unmapped open terrain presents many new challenges. Many
terrains are unstructured roads. The lack of highly structured components makes the roads segments more
complicated. In addition of the structured description, the types of the terrain are also the important
components.
There are many systems and methods to solve the problem. There are also many systems for autonomous
driving in off-road terrains. Such as [1] gets the depth information of the terrain using stereo vision and singleaxis ladar.
An off-road world model is proposed by [2], and an off-road obstacle model is also proposed by [3].In
practice the stereo vision needs accurate mounted and calibrated. The monocular vision is more easier to
manipulate. To get the depth information there are many methods just using the monocular vision . [4] could
get the depth information of the image by active focus. [5] could get the 3D surface curve using the monocular
vision. And there are many applications of monocular vision.
But in practical applications , the environments changes a lot. The terrain changes from mud to sand ,
from grass to cement. The Fig. 5 is shown. The world models and the road models must change. To avoid
changing the models or the set of parameters a boosted method is proposed in this paper. After data analysis
and class result statistics. The learning process could get the classifier.
Figure 1. Original Image
+
Corresponding author.
E-mail address: [email protected].
Figure 2. Image Segments
Figure 3. Classified Segments
Figure 4. Output results
2. Basic Idea
The robust parameters fitting all kinds of situations are calculated in the following way. First, a classifier
is constructed. Second, the classifier is trained with many situations. At last, the classifier is applied in
practical. The classifier is a adaboost based multiple tree classifier, which will be discussed in Section IV.
2.1 Training
First, the training data are collected in the following steps:
• Original image clustering into super pixels.
• Feature detection.
• Similarity estimate.
• Gather the feature matrix from images.
• Apply boosting to train the classifier.
Figure 5. Unstructured roads map
2.2 Classifing
After training, the classifier could be tested in practice.
• Original image clustering into super pixels.
• Feature detection.
• Classify.
First, a image is clustered into super pixels. Second, label the super pixels with road and non-road. As
much as the super pixels are labeled. Adaboost is applied to get the classifier. Then the classifier could be
used to segment the new images.
Super pixel is a group of points of the same image sharing the similar features. Using the method the
pixels could be boiled down to tens from millions. This could reduce the calculation of the later algorithm. [6]
is a good method to the super pixels image. In practice the clustering method is also a good way to get the
super pixels image. The clustering method is used in this paper.
3. Feature Selection
In this paper 58 feature parameters are chosen for the clustering process. There are 6 color
features(RGB+HSV), 15 LM textile features, 8 position features, 29 perspective features.
In [7] used as many features as can. So it can mostly classify the roads. But in our experiments 13 features
are good enough to get the fine results. [8] also using the multiple cues to detect the pedestrian from a moving
vehicle. It used the limited features which are shape and textile. Color is the important cues of the road.
Different features contributes different for the results. In practice the more is not the better. The more suitable
is the better. In this paper selects the 13 features. Fig. 6 is the selection of the 13 features.
Figure 6. Selected features of the super pixels
4. Adaboost Classifier
There are many classifers in practice. Such as SVM, neural networks and so on. But with the many road
situations there are conflicts data. And the adaboost could fit this situation. And more important the adaboost
could trained piece by piece. The classifer could be saved and retrained with the new data.
In December of 2006 adaboost is selected as one of the Top10 data mining method [9]. The adaboost
method is developing. There are many new versions of adaboost are proposed. Such as [10] proposed the
quadratic boosting.
5. Experiment Results
To valid the proposed method, a series of experiments are completed and the results show that the method
is efficient and robust.
The roads can be detected no more then 20ms and the detected results can be found as follows:
Figure 7. Result in the grass road
Figure 8. Result in the cement
Figure 9. Result in the cement of road conjunction
Figure10. Result in the mud and water ditch
Figure11. Result in the sand
Figure12. Result after rain
6. Discuss
After training the classifier could deal with all above situations in the same parameters. The classifier
could segment the different environments because of the classifier is trained with all the situations. And the
classifier could deal with more situations if trained with new data.
There are many ways to improve the method. The super pixels algorithm is just using the standard process.
If the special super pixels algorithm for road segment is proposed the classifier could get better results. And
the feature selection also should be discussed in future works. Because the better features are selected, the
better result should be got.
7. Acknowledgment
This work is supported by NSFC (Grant No. 90820302, No.90820015). This work is also supported by
Hunan Provincial Innovation Foundation for Postgraduate.
8. References
[1] R. Manduchi, A. Castano, A. Talukder, and L. Matthises. “Obstacle detection and terrain classification for
autonomous off-road navigation”. AutonomousRobots, vol. 18, pp 81–102, 2004.
[2] Tasi Hong Hong, Marilyn Abrams, Tommy Chang, and Michael Shneier. “An intelligent world model for
autonomous off-road driving” Computer Vision and Image Understanding, 2000 .
[3]
Joseph Weber and Larry Matthies, “An obstacle representation for off-road autonomous driving.” In Proceedings
of the Intelligent Vehicles Symposium, 1996.
[4] Moreno-Noguer F. , P.N. Belhumeur, and S.K. Nayar, “Active refocusing of image and videos,” Acm
Transcactions on Graphick, vol.26, 2007.
[5] Zeng J.G. and et al. “Reconstructing Symmetric curved surfaces from a single image and its application,”
Interactive Technologies and Sociotechnical Systems, pp. 204-213, 2006..
[6] Felzenszwalb, P. F. and D. P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of
Computer Vision, vol. 59, 2004..
[7] A. A. Efros Hoiem, and M. Hebert, “Recovering surface layout from an image,” International Journal of Computer
Vision, vol. 75, pp. 151-172, 2007.
[8] D. M. Gavrila and S. Munder, “Multi-cue pedestrian detection and tracking from a moving vehicle,” International
Journal of Computer Vision, vol. 73, pp. 41-59, 2007.
[9] Wu, X. D. et al. “Top 10 algorithms in data mining,” In Knowledge and Information Systems, vol.14 , pp. 1-37,
2008.
[10] T.V. Pham and A. W. M. Smeulders, “Quadratic boosting,” Pattern Recognition, vol. 41, pp. 331-341, 2008.