Abstract
A method of automatic obstacles detection is proposed which employs a camera mounted on a vehicle. Although various methods of obstacles detection have already been reported, they normally detect moving objects such as pedestrians and bicycles. In this paper, a method is proposed for detecting obstacles on a road, irrespective of moving or static, by the employment of the background modeling and the road region classification. The background modeling is often used to detect moving objects when a camera is static. In this paper, we apply it to the moving camera case to get foreground images. Then we extract the road region using SVM. In this road region, we carry out region classification. Then we can delete all the things which are not obstacles in the foreground images using the result of the region classification. In the performed experiments, it is shown that the proposed method is able to extract the shapes of both static and moving obstacles in a frontal view from a car.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Demonceaux, C., Kachi-Akkouche, D.: Robust obstacle detection with monocular vision based on motion analysis. In: Proc. IEEE Intelligent Vehicles Symposium, pp. 527–532 (2004)
Kruger, W., Enkelmann, W., Rossle, S.: Real-time estimation and tracking of optical flow vectors for obstacle detection. In: Proc. IEEE Intelligent Vehicles Symposium, pp. 304–309 (1995)
Lefaix, G., Marchand, E., Bouthemy, P.: Motion-based obstacle detection and tracking for car driving assistance. In: Proc. Pattern Recognition, pp. 74–77 (2002)
Broggi, A., Bertozzi, M., Fascioli, A., Sechi, M.: Shape-based pedestrian detection. In: Proc. IEEE Intelligent Vehicles Symposium, pp. 215–220 (2000)
Lutzeler, M., Dickmanns, E.D.: Road recognition with marveye. In: Proc. IEEE Intelligent Vehicles Symposium, pp. 341–346 (1998)
Kuehnle, A.: Symmetry-based vehicle location for AHS. In: Proc. SPIE-Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, vol. 2902, pp. 9–27 (1998)
Bertozzi, M., Broggi, A.: Gold: a parallel real-time stereo vision system for generic obstacle and lane detection. In: Proc. IEEE Image Processing, 62–81 (1998)
Labayrade, R., Aubert, D.: Robust and fast stereovision based obstacles detection for driving safety assistance. In: Proc. Machine Vision Applications, pp. 624–627 (2004)
Stauffer, C., Grimson, W.E.L.: Adaptive back-ground mixture models for real-time tracking. In: Proc. Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1995)
Zhou, S., Gong, J., Xiong, G., Chen, H., Iagnemma, K.: Road Detection Using Support Vector Machine based on Online Learning and Evaluation. In: Proc. IEEE Intelligent Vehicles Symposium, pp. 21–24 (2010)
Zhou, S., Iagnemma, K.: Self-supervised Learning Method for Unstructured Road Detection using Fuzzy Support Vector Machines. In: Proc. ICRA, pp. 1183–1189 (2010)
Haralick, R.M.: Statistical and Structural Approaches to Texture. Proc. IEEE 67, 786–804 (1979)
Yamaguchi, K., Kato, T., Ninomiya, Y.: Vehicle ego-motion estimation and moving object detection using a monocular camera. In: Proc. International Conference on Pattern Recognition, pp. 610–613 (2006)
Yamaguchi, K., Watanabe, A., Naito, T.: Road region estimation using a sequence of monocular images. In: Proc. International Conference on Pattern Recognition, pp. 1–4 (2008)
Yamaguchi, K., Kato, T., Ninomiya, Y.: Ego-motion estimation using a vehicle mounted monocular camera. Proc. The Institute of Electrical Engineers of Japan 129(12), 2213–2220 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qian, S., Tan, J.K., Kim, H., Ishikawa, S., Morie, T. (2013). Obstacles Extraction Using a Moving Camera. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-37484-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37483-8
Online ISBN: 978-3-642-37484-5
eBook Packages: Computer ScienceComputer Science (R0)