Real-time Road Obstacle Detection Using Association and Symmetry Recognition
This paper presents a fast road obstacle detection system based on association and symmetry. This approach consists to exploit the edges extracted from consecutive images acquired by a stereo sensor embedded in a moving vehicle. The algorithm contains three main components: edges detection, association detection and symmetry calculation. The edges detection is achieved by using the canny operator and point corner to extract all possible edges of different objects at the image. The association technique is used to exploit relationship between the edges of two consecutives images by combining it with the moment operator. The symmetry is used as road obstacle validation; the road obstacles like vehicle and pedestrian have a vertical symmetry. The proposed approach has been tested on different images. The provided results demonstrate the effectiveness of the proposed method.
. 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).
. M. El-Ansari, S. Mousset, and A. Bensrhair, “A new stereo matching approach for real-time road obstacle detection for situations with deteriorated visibility,” in Proc. IEEE Intelligent Vehicle Symposium, Eindhoven University of Technology, Eindhoven, The Netherlands, June 4-6 2008.
. Ilyas El Jaafari Mohamed El Ansari Lahcen Koutti Abdenbi Mazoul Ayoub El lahyani “ Fast spatio-temporal stereo matching for advanced driver assistance systems”, Neurocomputing Volume 194, 19 June 2016, Pages 24-33.
. Hu M.K. "Visual pattern recognition by moment invariants." IRE Transactions on Information Theory.Vol.8(2),pp.179-187,1962
. Gwenaëlle Toulminet, Massimo Bertozzi, Stéphane Mousset, Abdelaziz Bensrhair, and Alberto Broggi, Senior Member, IEEE “Vehicle Detection by Means of Stereo Vision-Based Obstacles Features Extraction and Monocular Pattern Analysis”
- There are currently no refbacks.