Data Association for Multiple Target Tracking: An Optimization Approach
In multiple target tracking the data association, observation to track fusion, is crucial and plays an important role for success of any tracking algorithm. The observation may be due to true target or may be clutter. In this paper, data association problem is viewed as an optimization problem and two methods, (i) using neural network and (ii) using the evolutionary algorithm, have been proposed and compared.
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- 1.Bar-shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, London (1989)Google Scholar
- 2.Gad, A., Majdi, F., Farooq, M.: A Comparison of Data Association Techniques for Target Tracking in Clutter. In: Proceedings of 5th International Conference on Information Fusion, pp. 1126–1133 (2002)Google Scholar
- 4.Zaveri, M.A., Desai, U.B., Merchant, S.: Interacting Multiple Model Based Tracking of Multiple Point Targets using Expectation Maximization Algorithm in Infrared image sequence. In: Proceedings of SPIE: Visual Communications and Image Processing (VCIP), vol. 5150, pp. 303–314 (2003)Google Scholar
- 6.Zaveri, M.A., et al.: Genetic Algorithm Based Data Association and Tracking of Multiple Point Targets. In: Proceedings of 10th National Conference on Communications (NCC - 2004), Banglore, India, pp. 414–418 (2004)Google Scholar