Abstract
Since closely moving targets exist extensively in the ground moving target tracking, the uncertainty of data association greatly increases making the measurement-to-track association more difficult. Especially, traditional multiple hypothesis tracking (MHT) has high false tracking rate and track swap. This paper first investigates the measurement based factor graph in data association, and gives the corresponding message passing algorithm. Then, a factor graph aided multiple hypothesis tracking (FGA-MHT) method is proposed, which introduces factor graph based m-best hypothesis producing technique and exploits factor graph based probability refinement algorithm to reduce the uncertainty of measurement-to-track association. Experiment results demonstrate that FGA-MHT reduces times of track swap and increases the correct data association rate in closely moving target tracking.
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References
Chong C Y, Castanon G, Cooprider N, et al. Efficient multiple hypothesis tracking by track segment graph. In: 12th International Conference on Information Fusion, Seattle, 2009. 2177–2184
Jordan M I. Graphical models. Stat Sci, 2004, 19: 140–155
Kschischang F R, Frey B J, Loeliger H. Factor graphs and the sum-product algorithm. IEEE Trans Inf Theory, 2001, 47: 498–519
Panakkal V P, Velmurugan R. Effective data association scheme for tracking closely moving targets using factor graphs. In: 17th National Conference on Communications, Bangalore, 2011. 1–5
Xu J, Li J, Xu S. Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering. Sci China Inf Sci, 2012, 55: 530–544
Lu S, Ma Y, Yang W. An effective data fusion and track prediction approach for multiple sensors. In: International Conference on Computational Intelligence and Software Engineering, Wuhan, 2010. 1–4
Cox I J, Hingorani S. An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans Patt Anal Mach Intell, 1996, 18: 138–150
Murty K G. An algorithm for ranking all the assignments in order of increasing cost. Oper Res, 1968, 16: 682–687
Reid D B. An algorithm for tracking multiple targets. IEEE Trans Automat Contr, 1979, 24: 843–854
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Wang, H., Sun, J., Lu, S. et al. Factor graph aided multiple hypothesis tracking. Sci. China Inf. Sci. 56, 1–6 (2013). https://doi.org/10.1007/s11432-013-5006-3
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DOI: https://doi.org/10.1007/s11432-013-5006-3