Stereo Vision-Based Road Obstacles Detection and Tracking

Authors

  • Khalid zebbara Laboratory Computer Systems and Vision, Department of Computer Science, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco

Keywords:

Obstacle detection, Vehicle detection, intelligent vehicle, road detection, obstacle tracking, Kalman filter.

Abstract

This paper presents a fast road obstacle detection system based on stereo vision. The algorithm contains three main components: road detection, obstacle detection and vehicle tracking. The road detection is achieved by using a small rectangular shape at bottom center of disparity image to extract the disparities of the road. The roadsides are located by using morphological processing and Hough transform. In the obstacle detection process, the objects can be easily located by the segmentation process. The vehicle tracking is achieved by the discrete Kalman filter. The proposed approach has been tested on different images. The provided results demonstrate the effectiveness of the proposed method.

References

. Aaqib Khalid, Tariq Umer, Muhammad Khalil Afzal, Sheraz Anjum, Hafiz Muhammad Asif : Autonomous data driven surveillance and rectification system using in-vehicle sensors for intelligent transportation systems (ITS) Computer Networks, Volume 139, 5 July 2018, Pages 109-118.

. Dylan Horne, Daniel J. Findley, Daniel G. Coble, Thomas J. Rickabaugh, James B. Martin :Evaluation of radar vehicle detection at four quadrant gate rail crossings, Journal of Rail Transport Planning & Management, Volume 6, Issue 2, September 2016, Pages 149-162

. Kirchner, A., Ameling, C.: Integrated obstacle and road traking using a laser scan-ner. In Intelligent Vehicles, USA, Oct. (2000).

. Parent, M., Crisostomo, M.: Collision avoidance for automated urban vehicles. In Intelligent Vehicles, Tokyo, Japan, June (2001).

. Tongtong Li, Changying Liu, Yang Liu, Tianhao Wang, Dapeng Yang : Binocular stereo vision calibration based on alternate adjustment algorithm Optik, Volume 173, November 2018, Pages 13-20.

. M. Dehnavi, M. Eshghi : Cost and power efficient FPGA based stereo vision system using directional graph transform, Journal of Visual Communication and Image Representation, Volume 56, October 2018, Pages 106-115

. Stefan Gehrig, Nicolai Schneider, Reto Stalder, Uwe Franke : Stereo vision during adverse weather — Using priors to increase robustness in real-time stereo vision, Image and Vision Computing, Volume 68, December 2017, Pages 28-39

. Xuanchen Zhang, Yuntao Song, Yang Yang, Hongtao Pan :Stereo vision based autonomous robot calibration : Robotics and Autonomous Systems, Volume 93, July 2017, Pages 43-51

. J. C. Rodríguez-Quiñonez, O. Sergiyenko, W. Flores-Fuentes, M. Rivas-lopez, P. Mercorelli : Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods’ approach, Opto-Electronics Review, Volume 25, Issue 1, May 2017, Pages 24-32.

. Hattori, H., Maki, A.: Stereo without depth search and metric calibration, Re-search & Development center, TOSHIBA Corporation.Kawasaki212-8582, Japan. IEEE (2000).

. Borja Bovcon, Rok Mandeljc, Janez Perš, Matej ristan :Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation, Robotics and Autonomous Systems, Volume 104, June 2018, Pages 1-13.

. Xuerui Dai : HybridNet: A fast vehicle detection system for autonomous driving, Signal Processing: Image Communication, Volume 70, February 2019, Pages 79-88.

. Coombs, D., Herman, M., Hong, T. H., Nashman, M.: Real-time obstacle avoidance using central ow divergence and peripheral ow. IEEE Transactions on Robotics and Automation, 14(1): 4959 , (1998).

. Ulrich, I., Nourbakhsh, I.: Appearance-based obstacle detection with monocular color vision. In: Proceedings of the 17th National Conference on Arti cial Intel-ligence and 12th Conference on Innovative Applications of Arti cial Intelligence. Austin, USA: 866871, AAAI Press, (2000).

. Saxena, A., Chung S. H., Ng, A. Y.: 3-D depth reconstruction from a single still image. International Journal of Computer Vision, 76(1): 5369 , (2008).

. Klarquist, W. N., Geisler, W. S.: Maximum likelihood depth from defocus for active vision. In: Proceedings of the InternationalConferen ce on Intelligent Robots and Systems. Washington D. C., USA: 3743797 , IEEE, (1995).

. Rajagopalan, A. N., Chaudhuri, S., Mudenagudi, U.: Depth estimation and image restoration using defocused stereo pairs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11): 15211525, (2004).

. Bellutta, P., Manduchi, R., Matthies, L., Owens, K., Rankin, A.: Terrain percep-tion for DEMO III. In: Proceedings of IEEE Conference on Intelligent Vehicles Symposium. Dearborn,USA: 38, IEEE, (2000).

. Rankin, A., Huertas, A., Matthies, L.: Evaluation of stereo vision obstacle detection algorithms for o -road autonomous navigation. AUVSI Unmanned Systems North America. Pasadena, USA: Jet Propulsion Laboratory, (2005).

. Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J.: Stan-ley, the robot that won the DARPA grand challenge. Journal of Robotics Systems, 23(9):661692, (2006).

. Konolige, K., Agrawal, M., Bolles, R. C., Cowan, C., Fischler M., Gerkey, B.: Outdoor mapping and navigation using stereo vision. In: Proceedings of the 10th International Symposium on Experimental Robotics. Rio de Janeiro, Brazil: 179190, Springer, (2006).

. Manduchi, R., Castano, A., Talukder, A., Matthies, L.: Obstacle detection and ter-rain classi cation for autonomous o -road navigation. Autonomous Robots, 18(1): 81102, (2005).

. Matthies, L., Maimone, M., Johnson, A., Cheng, Y., Willson R., Villalpando, C.: Computer vision on Mars. International Journal of Computer Vision, 2007, 75(1): 6792

. Broggi, A., Cara , C., Fedriga, R. I., Grisleri, P.: Obstacle detection with stereo vision for o -road vehicle navigation.In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: 6572, IEEE, (2005).

. Yifei Wang, Yuan Gao, Alin Achim, Naim Dahnoun: Robust obstacle detection based on a novel disparity calculation method and G-disparity Computer Vision and Image Understanding, Volume 123, June 2014, Pages 23-40

. Cara C., Cattani, S., Grisleri, P.: O -road path and obstacle detection using de-cision networks and stereo vision. IEEE Transactions on Intelligent Transportation Systems, 8(4): 607618, (2007).

. Soquet, N., Aubert, D., Hautiere, N.: Road Segmentation Supervised by an Ex-tended V-Disparity Algorithm for Autonomous Navigation. In: Intelligent Vehicles Symposium, IEEE (2007).

. Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection using evolutionary Gabor lter optimization, IEEE Trans. Intell. Transp. Syst., vol. 6, no. 2, pp. 125137, Jun. (2005).

. Broggi, A.,Cerri, P., Ghidoni, S., Grisleri, P., Jung, H. G.: A new approach to urban pedestrian detection for automatic braking, IEEE Trans. Intell. Transp. Syst., vol. 10, no. 4, pp. 594605, Dec. (2009).

. Lankton, S.: stereo-matching algorithm, (2008) http://www.shawnlankton.com.

. Daniel Viegas, Pedro Batista, Paulo Oliveira, Carlos Silvestre: Discrete-time distributed Kalman filter design for formations of autonomous vehicles Control Engineering Practice, Volume 75, June 2018, Pages 55-68.

. R. V. Garcia, P. C. P. M. Pardal, H. K. Kuga, M. C. Zanardi :Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter Advances in Space Research, In press, corrected proof, Available online 11 October 2018

. http://www.cvlibs.net/datasets/kitti/

Downloads

Published

2018-11-30

How to Cite

zebbara, K. (2018). Stereo Vision-Based Road Obstacles Detection and Tracking. International Journal of Computer (IJC), 31(1), 108–118. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1330

Issue

Section

Articles