Markov chain Monte Carlo data association for multi-target tracking

IEEE Transactions on Automatic Control

Volumn 54, Issue 3, 2009, Pages 481-497

  Oh, Songhwai ^a   Russell, Stuart ^b   Sastry, Shankar ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Joint probabilistic data association (JPDA); Markov chain Monte Carlo data association (MCMCDA); Multiple hypothesis tracking (MHT)

Indexed keywords

ALARM SYSTEMS; APPROXIMATION ALGORITHMS; MARKOV PROCESSES; MONTE CARLO METHODS; POLYNOMIAL APPROXIMATION; TARGETS; UNCERTAIN SYSTEMS;

ASSOCIATION PROBABILITIES; BAYESIAN FILTERS; CLUTTERED ENVIRONMENTS; DATA ASSOCIATION PROBLEMS; DETECTION PROBABILITIES; EXACT COMPUTATIONS; EXTENSIVE SIMULATIONS; EXTREME CONDITIONS; FALSE ALARM RATES; FULLY POLYNOMIAL RANDOMIZED APPROXIMATION SCHEMES; JOINT PROBABILISTIC DATA ASSOCIATION (JPDA); MARKOV CHAIN MONTE CARLO DATA ASSOCIATION (MCMCDA); MULTI SCANS; MULTI-TARGET TRACKING; MULTIPLE HYPOTHESIS TRACKING (MHT); NP-HARD; SIMULATION RESULTS; THEORETICAL RESULTS;

TARGET TRACKING;

EID: 63449088831     PISSN: 00189286     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAC.2009.2012975     Document Type: Article

References (51)

1. Y. Bar-Shalom and T. Fortmann, Tracking and Data Association. San Diego, CA: Academic Press, 1988.
2. I. Cox, "A review of statistical data association techniques for motion correspondence," Int. J. Comp. Vision, vol. 10, no. 1, pp. 53-66, 1993.
3. F. Dellaert, S. Seitz, C. Thorpe, and S. Thrun, "EM,MCMC, and chain flipping for structure from motion with unknown correspondence," Machine Learning, vol. 50, pp. 45-71, 2003.
4. G. Cybenko, V. Berk, V. Crespi, R. Gray, and G. Jiang, "An overview of process query systems," in Proc. SPIE, Sensors, Command, Control, Commun., Intell. (C3I) Technol. Homeland Secur. Homeland Def. III, Orlando, FL, Apr. 2004, vol. 5403, pp. 183-197.
5. S. Oh, L. Schenato, P. Chen, and S. Sastry, "Tracking and coordination of multiple agents using sensor networks: System design, algorithms and experiments," Proc. IEEE, vol. 95, no. 1, pp. 234-254, Jan. 2007.
6. R. Sittler, "An optimal data association problem on surveillance theory," IEEE Trans. Military Electron., vol. MIL-8, no. 2, pp. 125-139, Apr. 1964.
7. D. Reid, "An algorithm for tracking multiple targets," IEEE Trans. Automat. Control, vol. AC-24, no. 6, pp. 843-854, Dec. 1979.
8. A. Poore, "Multidimensional assignment and multitarget tracking," in Partitioning Data Sets, I. J. Cox, P. Hansen, and B. Julesz, Eds. Providence, RI: American Mathematical Society, 1995, pp. 169-196.
9. T. Kurien, "Issues in the design of practical multitarget tracking algorithms," in Multitarget-Multisensor Tracking: Advanced Applications, Y. Bar-Shalom, Ed. Norwood, MA: Artech House, 1990.
10. I. Cox and S. Hingorani, "An efficient implementation of Reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 2, pp. 138-150, Feb. 1996.
11. C. L. Morefield, "Application of 0-1 integer programming to multitarget tracking problems," IEEE Trans. Automat. Control, vol. AC-22, no. 3, pp. 302-312, Jun. 1971.
12. J. Collins and J. Uhlmann, "Efficient gating in data association with multivariate distributed states," IEEE Trans. Aerosp. Electron. Syst., vol. 28, no. 3, pp. 909-916, Jul. 1992.
13. M. Jerrum and A. Sinclair, "The Markov chain Monte Carlo method: An approach to approximate counting and integration," in Approximations for NP-Hard Problems, D. Hochbaum, Ed. Boston, MA: PWS Publishing, 1996.
14. L. Valiant, "The complexity of computing the permanent," Theor. Comp. Sci., vol. 8, pp. 189-201, 1979.
15. R. Fitzgerald, "Development of practical PDA logic for multi-target tracking by microprocessor," in Multitarget-Multisensor Tracking: Advanced Applications, Y. Bar-Shalom, Ed. Norwood, MA: Artech House, 1990.
16. J. Roecker and G. Phillis, "Suboptimal joint probabilistic data association," IEEE Trans. Aerosp. Electron. Syst., vol. 29, no. 2, pp. 510-517, Apr. 1993.
17. J. Roecker, "A class of near optimal JPDA algorithms," IEEE Trans. Aerosp. Electron. Syst., vol. 30, no. 2, pp. 504-510, Apr. 1994.
18. T. Huang and S. J. Russell, "Object identification in a Bayesian context," in Proc. 15th Int. Joint Conf. Artif. Intell., Nagoya, Japan, Aug. 1997.
19. R. Mahler, "A theoretical foundation for the stein-winter probability hypothesis density (phd) multi-target tracking approach," in Proc. MSS Nat. Symp. Sensor Data Fusion, San Antonio, TX, 2000, [CD ROM].
20. R. Mahler, "Multi-target Bayes filtering via first-order multi-target moments," IEEE Trans. Aerosp. Electron. Syst., vol. 39, no. 4, pp. 1152-1178, Oct. 2003.
21. R. Streit and T. Luginbuhl, "Maximum likelihood method for probabilistic multi-hypothesis tracking," in Proc. SPIE, Apr. 1994, vol. 2235, pp. 394-405.
22. L. Pao, "Multisensor multitarget mixture reduction algorithms for tracking," J. Guid., Control, Dyn., vol. 17, no. 6, pp. 1205-1211, Nov. 1994.
23. H. Pasula, S. J. Russell, M. Ostland, and Y. Ritov, "Tracking many objects with many sensors," in Proc. Int. Joint Conf. Artif. Intell. Stockholm, Sweden, 1999, pp. 1160-1171.
24. H. Pasula, "Identity Uncertainty," Ph.D. dissertation, Comp. Sci. Div., Univ. California: Computer, Berkeley, CA, 2003.
25. S. Cong, L. Hong, and D. Wicker, "Markov-chain Monte-Carlo approach for association probability evaluation," Proc. Inst. Elect. Eng., vol. 151, no. 2, pp. 185-193, Mar. 2004.
26. N. Bergman and A. Doucet, "Markov chain Monte Carlo data association for target tracking," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Istanbul, Turkey, Jun. 2000, pp. 11705-11708.
27. S. Oh, S. Russell, and S. Sastry, "Markov chain Monte Carlo data association for general multiple-target tracking problems," in Proc. IEEE Conf. Decision Control, Paradise Island, Bahamas, Dec. 2004, pp. 735-742.
28. W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. London, U.K.: Chapman and Hall, 1996.
29. I. Beichl and F. Sullivan, "The Metropolis algorithm," Comp. Sci. Eng., vol. 2, no. 1, pp. 65-69, 2000.
30. B. Eraker, "MCMC analysis of diffusion models with application to finance," J. Bus. Econom. Statist., vol. 19, no. 2, pp. 177-191, 2001.
31. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 6, pp. 721-741, Nov. 1984.
32. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller, "Equation of state calculation by fast computing machines," J. Chem. Phys., vol. 21, pp. 1087-1092, 1953.
33. W. Hastings, "Monte Carlo sampling methods using Markov chains and their applications," Biometrika, vol. 57, pp. 97-109, 1970.
34. G. Roberts, "Markov chain concepts related to sampling algorithms," in Markov Chain Monte Carlo in Practice, W. Gilks, S. Richardson, and D. Spiegelhalter, Eds. London, U.K.: Chapman and Hall, 1996.
35. P. Diaconis and D. Stroock, "Geometric bounds for eigenvalues of Markov chains," Annal. Appl. Prob., vol. 1, pp. 36-61, 1991.
36. H. Kushner, "Approximations to optimal nonlinear filters," IEEE Trans. Automat. Control, vol. AC-12, no. 5, pp. 546-556, Oct. 1967.
37. D. Schulz, W. Burgard, D. Fox, and A. Cremers, "Tracking multiple moving targets with a mobile robot using particle filters and statistical data association," in Proc. IEEE Int. Conf. Robot. Automat., 2001, pp. 1665-1670.
38. S. Julier and J. Uhlmann, "Unscented filtering and nonlinear estimation," Proc. IEEE, vol. 92, pp. 401-422, 2004.
39. H. Blom and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Trans. Automat. Control, vol. AC-33, no. 8, pp. 780-783, Aug. 1988.
40. A. Doucet, J. de Freitas, and N. Gordon, Sequential Monte Carlo Methods In Practice. New York: Springer Verlag, 2001.
41. M. Jerrum and A. Sinclair, "Polynomial-time approximation algorithms for the Ising model," SIAM J. Comp., vol. 22, pp. 1087-1116, 1993.
42. S. Oh and S. Sastry, "A polynomial-time approximation algorithm for joint probabilistic data association," in Proc. Amer. Control Conf. Portland, OR, Jun. 2005, vol. 2, pp. 1283-1288.
43. S. Oh, S. Russell, and S. Sastry, Markov Chain Monte Carlo Data Association for Multi-Target Tracking Univ. of California, Merced, CA, Tech. Rep. SoE TR 2008-001, 2008.
44. S. Kirkpatrick, C. Gelatt, and M. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983.
45. H. Pasula, B. Marthi, B. Milch, S. Russell, and I. Shpitser, "Identity uncertainty and citation matching," in Advances in Neural Information Processing Systems. Boston, MA: MIT Press, 2003, vol. 15.
46. A. McCallum, K. Nigam, and L. Ungar, "Efficient clustering of high-dimensional data sets with application to reference matching," in Proc. 6th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining (KDD) 2000, pp. 169-178.
47. B. Marthi, H. Pasula, S. Russell, and Y. Peres, "Decayed MCMC filtering," in Proc. UAI'02, Edmonton, AB, Canada, 2002, pp. 319-326.
48. I. Cox, Multiple Hypothesis Tracking Code Tech. Rep. [Online]. Available: http://www.ee.ucl.ac.uk/~icox/
49. S. Oh, Multiple Target Tracking for Surveillance Univ. of California, Berkeley, CA, Tech. Rep. UCB/ERL MO3/54, 2003.
50. C. van Rijsbergen, Information Retrieval. London, U.K.: Butterworths, 1979.
51. M. Bilenko and R. Mooney, "On evaluation and training-set construction for duplicate detection," in Proc. KDD'03 Workshop Data Cleaning, Record Linkage, Object Consolidation, Washington, DC, 2003, pp. 7-12.