Abstract
This paper proposes a new approach for detecting separation of a group of moving objects tracked by the method based on the particle filter. When some objects with overlaps come into the area filmed with the camera, they are treated as one object and tracked by a group of particles. When the object group separates into each object or some groups, the method fails in tracking or there are some objects which are not tracked. The proposed method detects the separation of the object group by the non-parametric Bayesian scheme. The method can perform the whole process in shorter time than the previous method and remains the same performance.
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References
Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)
Khan, Z., Balch, T., Dellaert, F.: MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets. IEEE Trans. on PAMI 27(11), 1805–1819 (2005)
Enda, N., Fukui, S., Kurahashi, W., Takechi, K., Iwahori, Y.: Robust Tracking Method to Intersections of Objects with Similar Patterns. Journal of IEEJ 130(9), 1588–1596 (2010) (in Japanese)
Takechi, K., Kurahashi, W., Iwahori, Y., Fukui, S.: Detection of Separation for Moving Objects Using Likelihood of Particle. In: Proc. of 2009 Tokai-Section Conf. on Elec. and Related Eng., p. O-202 (2009) (in Japanese)
Ueda, N., Nakano, R.: Deterministic Annealing EM Algorithm. Neural Networks 11(2), 271–282 (1998)
Render, E., Walker, H.: Mixture Densities. Maximum Likelihood and the EM Algorithm. SIAM Reiew 26(2) (1984)
Kimura, T., Nakada, Y., Doucet, A., Matsumoto, T.: Semi-supervised learning scheme using dirichlet process em-algorithm, IEICE Tech. Rep. 108(484), 77–82 (2009)
Neal, R.M.: Markov chain sampling methods for Dirichlet Process mixture models. Journal of Comp. and Graphical Statistics 9, 249–265 (2000)
Sethuraman, J.: A constructive definition of dirichlet priors. Statistica Sinica 4, 639–650 (1994)
Enda, N., Fujikawa, T., Fukui, S., Iwahori, Y.: Automatic Background Update for Initialization of Particle. In: Proc. of Forum on Information Technology 2008 (FIT 2008), pp. 325–328 (2008) (in Japanese)
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Takeda, Y., Fukui, S., Iwahori, Y., Woodham, R.J. (2011). Detecting Separation of Moving Objects Based on Non-parametric Bayesian Scheme for Tracking by Particle Filter. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23866-6_12
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DOI: https://doi.org/10.1007/978-3-642-23866-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23865-9
Online ISBN: 978-3-642-23866-6
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