Vision-based bicyclist detection and tracking for intelligent vehicles

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn , Issue , 2010, Pages 454-461

  Cho, Hyunggi ^a   Rybski, Paul E ^a   Zhang, Wende ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b GENERAL MOTORS CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDING BOX; COARSE-TO-FINE; COMPLEMENTARY DETECTION; CORNER POINT; DETECTION AND TRACKING; DETECTION PHASE; DETECTION PROCESS; DISCRIMINATIVE FEATURES; HARRIS CORNER DETECTOR; INTEGRAL HISTOGRAM; INTELLIGENT VEHICLES; LINEAR SUPPORT VECTOR MACHINES; LUCAS-KANADE TRACKER; MULTIPLE PATCHES; MULTIPLE VIEWS; NOVEL METHODS; OBJECT CLASSIFICATION; QUANTITATIVE EXPERIMENTS; SIDE VIEW; URBAN TRAFFIC; VISION BASED;

CLUSTERING ALGORITHMS; GRAPHIC METHODS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; OPTICAL FLOWS; STATISTICAL METHODS; TRACKING (POSITION); VEHICLES;

DETECTORS;

EID: 77956527896     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVS.2010.5548063     Document Type: Conference Paper

References (25)

1. M. Bertozzi. A. Broggi, M. Cellario, A. Fascioli, P. Lombardi, and M. Porta. Artificial vision in road vehicles. Proceedings of the IEEE, 90(7):1258-1271, 2002.
2. H. Cheng, N. Zheng, and J. Qin. Pedestrian detection using sparse gabor filters and support vector machine. IEEE Intelligent Vehicle Symposium, pages 583-587, 2005.
3. D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence. 25(5): 564-577, 2003.
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. Conference on Computer Vision and Pattern Recognition, 2005.
5. P. Dollar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. Conference on Computer Vision and Pattern Recognition, 2009.
6. M. Enzweiler and D. Gavrila. Monocular pedestrian detection: Survey and experiments. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2008.
7. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory: Eurocolt '95, pages 23-37, 1995.
8. T. Gandhi and M. M. Trivedi. Pedestrian protection systems: Issues, survey, and challenges. IEEE Transaction on Intelligent Transportation System, 8(3):413-430, 2007.
9. D. M. Gavrila. Sensor-based pedestrian protection. IEEE Intelligent System, 16(6):77 81, 2001.
10. D. M. Gavrila, J. Giebel, and S. Munder. Vision-based pedestrian detection: The protector system. IEEE Intelligent Vehicle Symposium, pages 13-18, 2004.
11. C. Harris and M. Stephens. A combined corner and edge detector. Alvey Vision Conference, pages 147-152, 1988.
12. Z. Li, L. L. K. Wang, and F. Wang. A review on vision-based pedestrian detection for intelligent vehicles. Conference on Vehicular Electronics and Safety, 2006.
13. J. Llinas and D. Hall. An introduction to multi-sensor data fusion. International Symposium on Circuits and Systems, 1998.
14. B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence, 1981.
15. C. Papageorgiou and T. Poggio. A trainable system for object detection. International Journal of Computer Vision, 38(1):15-33, 2000.
16. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 1999.
17. F. Porikli. Integral histogram: A fast way to extract histograms in cartesian spaces. Conference on Computer Vision and Pattern Recognition, 2005.
18. P. Sabzmeydani and G. Mori. Delecting pedestrians by learning shapelet features. Conference on Computer Vision and Pattern Recognition, 2007.
19. J. Shi and C. Tomasi. Good features to track. Conference on Computer Vision and Pattern Recognition, pages 593-600, 1994.
20. K. Smith, D. Gatica-Perez, and J. M. Odobez. Using particles to track varying numbers of interacting people. Conference on Computer Vision and Pattern Recognition, 2005.
21. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. Conference on Computer Vision and Pattern Recognition, 2001.
22. P. Viola, M. J. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision, 63(2):153-161, 2005.
23. U. von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395-416, 2007.
24. B. Wu and R. Nevatia. Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. International Journal of Computer Vision, 1:90-97, 2005.
25. Q. Zhu, S. Avidan, M. Yen, and K. Cheng. Fast human detection using a cascade of histograms of oriented gradients. Conference on Computer Vision and Pattern Recognition, 2006.