Feature-based multisensor fusion using Bayes formula for pedestrian classification in outdoor environments

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn , Issue , 2007, Pages 62-67

  Pangop, Laurence Ngako ^a   Chausse, Frederic ^a   Cornou, Sebastien ^b   Chapuis, Roland ^a  

^a UNIVERSITÉ BLAISE PASCAL   (France)

^b RENAULT   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CAMERAS; DATA FUSION; DATA HANDLING; FEATURE EXTRACTION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LASER APPLICATIONS; OBJECT DETECTION; PEDESTRIAN SAFETY; SCANNING; VEHICLES;

BAYESIAN COMBINATION; KNOWLEDGE INTEGRATION; MULTISENSOR DATA FUSION; PEDESTRIAN DETECTION SYSTEM; PROBABILISTIC FRAMEWORK; RECEIVER OPERATING CHARACTERISTICS; SENSOR DATA PROCESSING; TRACKING AND CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 47849103722     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ivs.2007.4290092     Document Type: Conference Paper

References (12)

1. M. Bertozzi, A. Broggi, R. Chapuis, F. Chausse, A. Fascioli, and A. Tibaldi, Shape-based pedestrian detection and localization. In Procs. IEEE Intl. Conf. on Intelligent Transportation Systems 2003, pp. 328-333, Shangai, China, Oct. 2003.
2. A. Broggi, M. Bertozzi, A. Fascoli, and M. Sechi, Shape-based pedestrian detection. In Proc. IEEE Intelligent Vehicle Symposium 2000, pp. 215-220, Dearborn, USA, 2000.
3. U. Franke, D.M. Gavrila, S. Grzig, F. Lindner, F. Paetzold, and C. Whler, Autonomous driving goes downtown. IEEE intelligent Systems, 13(6), pp. 40-48, 1998.
4. KC Fuerstenberg and U. Lage, Pedestrian Detection and Classification by Laserscanners. 9th EAEC International Congress, Paris, France, June, 2003.
5. T. Gandhi and M. Trived, Pedestrian Collision Avoidance Systems: A Survey of Computer Vision Based Recent Studies. In Proc. of the IEEE Intelligent Transportation Systems Conference, pp. 976-981, Toronto, Canada,2006.
6. D.M. Gavrila, Sensor-based Pedestrian Protection. IEEE Intelligent Systems, vol. 16, NR.6, pp. 77-81, 2001.
7. J. Guivant, E. Nebot, and S. Baiker, Simultaneous localization and map building using natural features and absolute information. Robotics and Autonomous Systems, vol. 40, pp. 79-90, 2002.
8. A. Mendes and U. Numes, Situation-based Multi-target Detection and Tracking with Laserscanner in Outdoor Semi-structured Environment. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, 2004.
9. M. Szarvas, U. Sakai and J. Ogata, Real-time Pedestrian Detection Using Lidar and Convolutional Neural Networks. In IEEE Intelligent Vehicles Symposium 2006, Tokyo, Japan,2006.
10. P.Viola and M.Jones, Rapid object detection using a boosted cascade of simple features.In IEEE Conference on Computer Vision and Pattern Recognition,2001.
11. P.Viola, M. Jones and D. Snow, Detecting Pedestrians Using Patterns of Motion and Appearance. In Proc. of the International Conference on Computer Vision (ICCV). Nice, France,2003.
12. l. Zhao and C. Thorpe, Stereo- and neural network-based pedestrian detection. IEEE Transaction on Intelligent Transportation Systems, 1(3), 2000.