Abstract
This paper introduces a particle filter algorithm determining the measurement-track association problem in multi-target tracking. This scheme is important in providing a computationally feasible alternative to complete enumeration of JPDA which is intractable. We have proved that given an artificial measurement and track’s configuration, particle filter scheme converges to a proper plot in a finite number of iterations. Also, a proper plot which is not the global solution can be corrected by re-initializing one or more times. In this light, even if the performance is enhanced by using the particle filter, we also note that the difficulty in tuning the parameters of the particle filter is critical aspect of this scheme. The difficulty can, however, be overcome by developing suitable automatic instruments that will iteratively verify convergence as the network parameters vary.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alspach, D.L.: A Gaussian Sum Approach to the Multi-Target Identification Tracking Problem. Automatica 11, 285–296 (1975)
Bar-Shalom, Y.: Extension of the Probabilistic Data Association Filter in Multi-Target Tracking. In: Proceedings of the 5th Symposium on Nonlinear Estimation, pp. 16–21 (1974)
Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. on Automat. Contr. 24, 843–854 (1979)
Lee, Y.: Development of relaxation scheme for the Multiple Targets in a Cluttered Environment
Sengupta, D., Iltis, D., Neural, R.A.: solution to the multitarget tracking data association problem. IEEE Trans. on AES 25, 96–108 (1999)
Fortmann, T.E., Bar-Shalom, Y., Scheffe, M.: Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association. IEEE J. Oceanic Engineering 8, 173–184 (1983)
Lee, Y.: Adaptive Data Association for Multi-target Tracking using relaxation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 552–561. Springer, Heidelberg (2005)
Lee, Y., Seo, J.H., Lee, J.G.: A Study on the TWS Tracking Filter for Multi-Target Tracking. Journal of KIEE 41(4), 411–421 (2004)
Isard, M., Blake, A.: CONDENSATION-conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29(1), 5–28 (1998)
Black, M.J., Jepson, A.D.: A Probabilistic Framework for Matching Temporal Trajectories: CONDENSATION-Based Recognition of Gestures and Expressions. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 909–924. Springer, Heidelberg (1998)
Cichocki, A., Unbenhauen, R.: Neural networks for optimization and signal processing, pp. 520–526. Wiley, New York (1993)
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Trans. ASME (J. Basic Eng.) 82, 34–45 (1960)
Singer, R.A.: Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Trans. Aerospace and Electronic Systems 6, 473–483 (1970)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Lee, Y.W. (2007). Development of the Multi-target Tracking Scheme Using Particle Filter. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_144
Download citation
DOI: https://doi.org/10.1007/978-3-540-72395-0_144
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
eBook Packages: Computer ScienceComputer Science (R0)