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A study on the Optimal Adaptive Data Association for Multi-Target Tracking  

Lee, Yang-Weon (호남대학교 정보통신학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.6, no.8, 2002 , pp. 1146-1152 More about this Journal
Abstract
This paper proposed a scheme for finding an optimal adaptive data association for multi-target between measurements and tracks. First, we assume the relationships between measurements as Mrkov Random Field. Also assumed a priori of the associations as a Gibbs distribution. Based on these assumptions, it was possible to reduce the MAP estimate of the association matrix to the energy minimization problem. After then, we defined an energy function over the measurement space, that may incorporate most of the important natural constraints. Through the experiments, we analyzed and compared this algorithm with other representative algorithms. The result is that it is stable, robust, fast enough for real timecomputation, as well as more accurate than other methods.
Keywords
Multi-target tracking; Data Association;
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  • Reference
1 Kuczewski R., ``Neural network approaches to multitarget tracking,'' In proceedings of the IEEE ICNN conference, 1987
2 Lee Y. W., and Jeong H. ,``A Neural Network Approach to the Optimal Data Association in Multi-Target Tracking,'' Proc. of WCNN'95, INNS Press
3 Alspach D. L., ''A Gaussian sum approach to multi-target identification tracking problem,' Automatica, Vol. 11, pp. 285-296, May 1975   DOI   ScienceOn
4 Fortmann T. E., Bar-Shalom Y., and Scheffe M.,"Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association," IEEE J. Oceanic Engineering, Vol. OE-8, pp.173-184, July 1983   DOI
5 Luenberger D. G., Linear and Nonlinear Programming, Addition-wesley Publishing Co., 1984
6 Kalman R.E.,"A new approach to linear filtering and prediction problems," Trans. ASME, (J. Basic Eng.,) Vol.82, pp34-45, Mar. 1960
7 Singer, R.A., "Estimating optimal tracking filter performance for manned maneuvering targets," IEEE Transactions on Aerospace and Electronic Systems, Vol. 6, , pp. 473-483, July 1970   DOI   ScienceOn
8 Fortmann T. E., Bar-Shalom Y., Tracking and Data Association. Orland Acdemic Press, Inc. p224, 1988
9 Sengupta, D., and Iltis, R. A.,``Neural solution to the multitarget tracking data association problem,'' IEEE Trans. on AES, AES-25, pp. 96-108, Jan. 1989
10 Hiriart-Urruty J. B. and Lemarrecchal C. Convex Analysis and Minimization Algorithms I, Springer-Verlag, 1993
11 Bar-Shalom Y., ``Extension of probabilistic data associatiation filter in multitarget tracking,'' in Proc. 5th Symp. NonlinearEstimation Theory and its Application, pp. 16-21, Sept. 1974
12 Reid D. B., "An algorithm for tracking multiple targets,'' IEEE Trans. on Automat. Contr., Vol. 24, ,pp. 843-854, Dec. 1979   DOI
13 Won C.S. and Derin H., "Unsupervised segmentation of noisy and textured images using Markov random fields," CVGIP: Graphical Models and Image Processing, Vol. 54, No. 4, pp. 308-328, July, 1992   DOI