Kernel and feature selection for visible and infrared based obstacle recognition

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn , Issue , 2008, Pages 1130-1135

  Apatean, Anca ^a   Rogozan, Alexandrina ^b   Bensrhair, Abdelaziz ^b  

^a TECHNICAL UNIVERSITY OF CLUJ NAPOCA   (Romania)

^b UNIVERSITÉ ET INSA DE ROUEN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION TIME; FEATURE SELECTION ALGORITHMS; FEATURE SELECTIONS; FUSION MODELS; INFRARED IMAGES; INFRARED SPECTRUM; LINEAR COMBINATIONS; OBSTACLE RECOGNITION;

INFRARED IMAGING; INFRARED SPECTROSCOPY; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; VEHICLE LOCATING SYSTEMS;

FEATURE EXTRACTION;

EID: 60749125323     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2008.4732711     Document Type: Conference Paper

References (14)

1. A, Bensrhair et al.,'" Stereo Vision-based Feature Extraction for vehicle detection'", In Procs. IEEE Intelligent Vehicles Symposium., volume 2, pages 465-470, June 17-21, 2002.
2. C. Papgeorgiou et al.,"'A Trainable Pedestrian Detection system'", IEEE International Conference on Intelligent Vehicles, 241-246, Germany, 1998.
3. G. Grubb et al.,"'3D Vision Sensing for Improved Pedestrian Safety'", IEEE Intelligent Vehicle Symposium, pp. 19-24, Parma, Italy, 2004.
4. P. Viola et al., '"Detecting Pedestrians using Patterns of Motion and Appearance'", Proc. of the International Conference on Computer Vision, pp. 734-741, Nice, France, 2003.
5. L. Zhao, C. Thorpe, '"Stereo and Neural Network-based Pedestrian Detection"', IEEE Transactions on Intelligent Transportation Systems, 1(3), 2000.
6. A. Broggi et al.,'" Shape-based Pedestrian Detection'", October 17, 2000.
7. F. Suard et al., '"Pedestrian Detection using Stereo-vision and Graph Kernels'", In Procs. IEEE Intelligent Vehicles Symposium, pp. 267- 272, June 2005.
8. D M. Gavrila et al., '"Sensors and system architecture for vulnerable road users protection, Deliverable Dll: Algorithms for detection and classification of VRUs'",Information Society Technologies: Systems and Services for the Citizen, 2004.
9. D. M. Gavrila et al., '"A multi-sensor approach for the protection of vulnerable traffic participants - the PROTECTOR project"', IEEE Instrumentation and Measurement Technology Conference, vol. 3, pp. 2044-2048, 2001.
10. Z. Sun et al.,'"Monocular Precrash Vehicle Detection: Features and Classifiers'", IEEE Transactions on Image Processing, vol. 15, no.7, pp2019-2034, July 2006.
11. O. Chapelle et al., '"Choosing multiple parameters for support vector machines'", Machine Learning 46(1/3), 131-159, 2002.
12. W. K. Pratt, Digital Image Processing: PIKS Inside, John Wiley & Sons, Inc., New York, NY, USA, 0471374075; 2001.
13. M. M. Galloway, '"Texture Analysis using Graylevel Runlengths'", Computer, Graphics and Image Processing, vol. 4, pages 172-179, 1975.
14. G. Toulminet - Research activities at http://asi.insa-rouen.fr/en- seignants~gtoulminet