Abstract
This paper introduces an adaptive algorithm determining the measurement-track association problem in multi-target tracking. We model the target and measurement relationships and then define a MAP estimate for the optimal association. Based on this model, we introduce an energy function defined over the measurement space, that incorporates the natural constraints for target tracking. To find the minimizer of the energy function, we derived a new adaptive algorithm by introducing the Lagrange multipliers and local dual theory. Through the experiments, we show that this algorithm is stable and works well in general environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alspach, D.L.: A Gaussian sum approach to multi-target identification tracking problem. Automatica 11, 285–296 (1975)
Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. on Automat. Contr. 24, 843–854 (1979); J. Basic Eng. 82, 34–45 (1960)
Bar-Shalom, Y.: Extension of probabilistic data associatiation filter in multitarget tracking. In: Proc. 5th Symp. Nonlinear Estimation Theory and its Application, pp. 16–21 (1974)
Sengupta, D., Iltis, R.A.: Neural solution to the multitarget tracking data association problem. IEEE Trans. on AES, AES-25, 96–108 (1989)
Kuczewski, R.: Neural network approaches to multitarget tracking. In: Proceedings of the IEEE ICNN conference (1987)
Fortmann, T.E., Bar-Shalom, Y., Scheffe, M.: Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association. IEEE J. Oceanic Engineering, OE-8, 173–184 (1983)
Fitzgerald, R.J.: Development of practical PDA logic for multitarget tracking by microprocessor. In: Proceedings of the American Controls Conference, Seattle, Wash., pp. 889–898 (1986)
Fortmann, T.E., Bar-Shalom, Y.: Tracking and Data Association. Orland Acdemic Press (1988)
Lee, Y.W., Jeong, H.: A Neural Network Approach to the Optimal Data Association in Multi-Target Tracking. In: Proc. of WCNN 1995. INNS Press (1995)
Luenberger, D.G.: Linear and Nonlinear Programming. Addison-wesley Publishing Co., Reading (1984)
Hiriart-Urruty, J.B., Lemarrecchal, C.: Convex Analysis and Minimization Algorithms I. Springer, Heidelberg (1993)
Cichocki, A., Unbenhauen, R.: Neural networks for optimization and signal processing. Wiley, New York (1993)
Aarts, E., Korst, J.: Simulated annealing and Boltzmann Machines. Wily, New York (1989)
Singer, R.A.: Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems 6, 473–483 (1970)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME (J. Basic Eng.)Â 82 (1960)
Platt, J.C., Barr, A.H.: Constrained Differential Optimization. In: Proceedings of the 1987 IEEE NIPS Conf., Denver (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, YW. (2005). Adaptive Data Association for Multi-target Tracking Using Relaxation. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_58
Download citation
DOI: https://doi.org/10.1007/11538059_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
Online ISBN: 978-3-540-31902-3
eBook Packages: Computer ScienceComputer Science (R0)