Real-time multi-vehicle tracking based on feature detection and color probability model

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn , Issue , 2010, Pages 981-986

  Huang, Lili ^a   Barth, Matthew ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CORNER FEATURE; FEATURE DETECTION; GLOBAL FEATURE; HIGH DENSITY; LOCAL FEATURE; MOVING CAMERAS; MOVING OBJECTS; MULTI-VEHICLES; POSITION ESTIMATES; PROBABILITY MODELS; SPLITTING AND MERGING; SURVEILLANCE VIDEO; TRAFFIC SURVEILLANCE; VEHICLE POSITION; VEHICLE TRACKING; VIDEO CLIPS;

COLOR; COMPUTER VISION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; PROBABILITY; SECURITY SYSTEMS; VEHICLES;

TRACKING (POSITION);

EID: 77956506032     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVS.2010.5548060     Document Type: Conference Paper

References (17)

1. Benjamin Coifman, David Beymer, Philip McLauchlan and Jitendra Malik, "A Real-time Computer Vision System for Vehicle Tracking and Traffic Surveillance," Transportation Research Part C 6, pp. 271-288, 1998.
2. ZuWhan Kim and Jitendra Malik, "Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking," IEEE International Conference on Computer Vision, vol. 1, pp. 524-531, 2003.
3. G. Antonini and J. P. Thiran, "Counting Pedestrians in Video Sequences Using Trejectory Clustering," IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, pp. 1008-1020, 2006.
4. W. Forstner and E. Gulch, "A Fast Operator for Detection and Precise Location of Distinct Points, Corners, and Centers of Circular Features," Proceedings Intercommision Conference on Fast Processing of Photogrammetric Data, pp. 281-305, 1987.
5. ZuWhan Kim, "Real Time Object Tracking Based on Dynamic Feature Grouping with Background Subtraction," IEEE Conf. Computer Vision and Pattern Recognition, 2008.
6. O. Sidla, Y. Lypetskyy, N. Brandle, and S. Seer, "Pedestrian Detection and Tracking for Counting Applications in Crowded Situation," Proc. IEEE International Conference on Video and Signal Based Surveillance, 2006.
7. S. Kamino, Y. Matsushita, K. Ikeuchi, and M. Sakauchi, "Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model," 15th International Conference on Pattern Recognition, vol. 1, 2000.
8. S. Gupte, O. Masoud, R.F.K. Martin and N.P. Papanikolopoulos, "Detection and Classification of Vehicles," IEEE Transactions on Intelligent Transportation Systems, 3(1):37-47, Mar. 2002.
9. D. Koller, J. Webber, and J. Malik, "Robust Multiple Car Tracking with Occlusion Reasoning," European Conference on Computer Vision, pp. 189-196, 1994.
10. C. Schlosser, J. Reitberger, and S. Hinz, "Automatic Car Detection in High Resolution Urban Scenes Based on An Adaptive 3D Model," EEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas, pp. 98-107, 2003.
11. Swain, M. J. and Ballard, D. H., "Color indexing," 7th International Conference on Computer Vision, vol. 7, pp. 11-32, 1991.
12. Comaniciu, D., Ramesh, V. and Meer, P., "Real-time Tracking of Non-rigid Objects Using Mean Shift, IEEE Computer Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 142-149, 2000.
13. C. Stauffer and W.E.L. Grimson, "Adaptive Background Mixture Models for Real-time Tracking," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
14. Carlo Tomasi and Tekeo kanade, "Detection and Tracking of Point Features," Technical Report CMU-CS-91-132.
15. Bruce D. Lucas and Takeo Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," International Joint Conference on Artificial Intelligence, pp. 674-679, 1981.
16. Jie Yu, Dirk Farin and Hartmut S. Loos, "Multi-cue Based Visual Tracking in Clutter Scenes with Occlusions," 6th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 158-163, 2009.
17. D.W. Scott, Multivariate Density Estimation, New York: Wiley, 1992.