Tracking a minimum bounding rectangle based on extreme value theory

IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

Volumn , Issue , 2010, Pages 56-61

  Baum, Marcus ^a   Hanebeck, Uwe D ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVE GAUSSIAN NOISE; BASIC IDEA; BAYESIAN ESTIMATORS; EXTENDED OBJECTS; EXTREME VALUE THEORY; GROUP MEMBERS; GROUP TARGETS; LINEAR MEASUREMENT EQUATION; MINIMUM BOUNDING RECTANGLE; NOISY MEASUREMENTS; POINT FEATURES; POINT SET; SIMULATION RESULT;

GAUSSIAN NOISE (ELECTRONIC); GEOMETRY; INTELLIGENT SYSTEMS;

ESTIMATION;

EID: 78649246101     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MFI.2010.5604456     Document Type: Conference Paper

References (18)

1. D. Hall and J. Llinas, Handbook of Multisensor Data Fusion. CRC Press, May 2001.
2. M. J. Waxman and O. E. Drummond, "A Bibliography of Cluster (Group) Tracking," Signal and Data Processing of Small Targets 2004, vol. 5428, no. 1, pp. 551-560, 2004.
3. K. Gilholm and D. Salmond, "Spatial Distribution Model for Tracking Extended Objects," Radar, Sonar and Navigation, IEE Proceedings, vol. 152, no. 5, pp. 364-371, October 2005.
4. M. Baum and U. D. Hanebeck, "Random Hypersurface Models for Extended Object Tracking," in Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009), Ajman, United Arab Emirates, Dec. 2009.
5. K. Gilholm, S. Godsill, S. Maskell, and D. Salmond, "Poisson Models for Extended Target and Group Tracking," in SPIE: Signal and Data Processing of Small Targets, 2005.
6. M. Bensic and K. Sabo, "Border Estimation of a Two-Dimensional Uniform Distribution if Data are Measured with Additive Error," Statistics - A Journal of Theoretical and Applied Statistics, vol. 41, pp. 311-319(9), August 2007.
7. M. Baum and U. D. Hanebeck, "Tracking an Extended Object Modeled as an Axis-Aligned Rectangle," in 4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), Lübeck, Germany, October 2009.
8. J. Vermaak, N. Ikoma, and S. Godsill, "Extended object tracking using particle techniques," Aerospace Conference, 2004. Proceedings. 2004 IEEE, vol. 3, pp. -1885 Vol.3, March 2004.
9. -, "Sequential Monte Carlo Framework for Extended Object Tracking," IEE Proceedings on Radar, Sonar and Navigation, vol. 152, no. 5, pp. 353-363, October 2005.
10. N. Ikoma and S. Godsill, "Extended Object Tracking with Unknown Association, Missing Observations, and Clutter using Particle Filters," in IEEE Workshop on Statistical Signal Processing, 2003, pp. 502-505.
11. Z. Zhang, "Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting," Image and Vision Computing, vol. 15, no. 1, pp. 59-76, 1997.
12. J. Porrill, "Fitting Ellipses and Predicting Confidence Envelopes using a Bias Corrected Kalman filter," Image Vision Comput., vol. 8, no. 1, pp. 37-41, 1990.
13. T. Ellis, A. Abbood, and B. Brillault, "Ellipse detection and matching with uncertainty," Image Vision Comput., vol. 10, no. 5, pp. 271-276, 1992.
14. L. de Haan and A. Ferreira, Extreme Value Theory - An Introduction. Springer Verlag, 2006.
15. P. Embrechts, "Extreme Value Theory: Potential And Limitations As An Integrated Risk Management Tool," Derivatives Use, Trading & Regulation, vol. 6, 2000.
16. P. Embrechts, T. Mikosch, and C. Klüppelberg, Modelling Extremal Events: For Insurance and Finance. London, UK: Springer, 1997.
17. D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, 1st ed. Wiley & Sons, August 2006.
18. J. Kotecha and P. Djuric, "Gaussian Particle Filtering," IEEE Transactions on Signal Processing, vol. 51, no. 10, pp. 2592-2601, Oct. 2003.