Bar-Shalom Y. Tracking methods in a multitarget environment. IEEE Transactions on Automatic Control, 1978, 23(4): 618-626
[2]
Koch W, van Keuk G. Multiple hypothesis track maintenance with possibly unresolved measurements. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(3): 883-892
[3]
Feldmann M, Fr？nken D, Koch W. Tracking of extended objects and group targets using random matrices. IEEE Transactions on Signal Processing, 2011, 59(4): 1409-1420
[4]
Baum M, Hanebeck U D. Shape tracking of extended objects and group targets with star-convex RHMs. In: Proceedings of the 14th International Conference on Information Fusion. Chicago, Illinois, USA: IEEE, 2011. 338-345
[5]
Baum M, Noack B, Hanebeck U D. Extended object and group tracking with elliptic random hypersurface models. In: Proceedings of the 13th International Conference on Information Fusion. Edinburg, UK: IEEE, 2010. 1-8
[6]
Mahler R. PHD filters for nonstandard target I: extended targets. In: Proceedings of the 12th International Conference on Information Fusion. Seattle, WA, USA: ISIF, 2009. 915-921
[7]
Lundquist C, Granstr？m K, Orguner U. Estimating the shape of targets with a PHD filter. In: Proceedings of the 14th International Conference on Information Fusion. Chicago, Illinois, USA: IEEE, 2011. 49-56
[8]
Lian Feng, Han Chong-Zhao, Liu Wei-Feng, Yuan Xiang-Hui. Tracking partly resolvable group targets using SMC-PHDF. Acta Automatica Sinica, 2010, 36(5): 731-741 (连峰, 韩崇昭, 刘伟峰, 元向辉. 基于SMC-PHDF 的部分可分辨的群目标跟踪算法. 自动化学报, 2010, 36(5): 731-741)
[9]
Joo S W, Chellpa R. A multiple-hypothesis approach for multiobject visual tracking. IEEE Transactions on Image Processing, 2007, 16(11): 2849-2854
[10]
Gordon N J, Samlond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceeding Control Theory and Application, 1993, 140(2): 107-113
[11]
Khan Z, Balch T, Dellaert F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11): 1805-1819
[12]
Liu Wei-Feng. Research on Multitarget Tracking Algorithm Based on Random Finite Sets and Finite Mixture Models [Ph.D. dissertation], Xi'an Jiaotong University, China, 2009 (刘伟峰. 基于随机有限集和有限混合模型的多目标跟踪算法研究 [博士学位论文], 西安交通大学, 中国, 2009)
[13]
Hastings W K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 1970, 57(1): 97-109
[14]
Reid D B. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 1979, 24(6): 843-854
[15]
Koch J W. Bayesian approach to extended object and cluster tracking using random matrices. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1042-1059
[16]
Richter E, Obst M, Noll M, Wanielik G. Tracking multiple extended objects — a Markov chain Monte Carlo approach. In: Proceedings of the 14th International Conference on Information Fusion. Chicago. Illinois, USA: IEEE, 2011. 314-321
[17]
Baum M, Hanebeck U D. Random hypersurface models for extended object tracking. In: Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology. Ajman, United Arab Emirates: IEEE, 2009. 178-183
[18]
Orguner U. Lundquist C, Granstr？m K. Extended target tracking with a cardinalized probability hypothesis density filter. In: Proceedings of the 14th International Conference on Information Fusion. Chicago, Illinois, USA: IEEE, 2011. 65-72
[19]
Rasmussen C, Hager G D. Probabilistic data association methods for tracking complex visual objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 560-576
[20]
Fleuret F, Berclaz J, Lengagne R, Fua P. Multicamera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 267-282
[21]
Oh S, Russell S, Sastry S. Markov chain Monte Carlo data association for multi-target tracking. IEEE Transactions on Automatic Control, 2009, 54(3): 481-497
[22]
Liu W F, Han C Z. Multitarget tracking algorithm based on finite mixture models and equivalent measurement. In: Proceedings of the 11th International Conference on Information Fusion. Cologne, Germany: IEEE, 2008. 1544-1551
[23]
Green P J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 1995, 82(4): 711-732
