Abstract
In order to track an occluded target in an image sequence, the Bayesian decision theory is, here, introduced to the problem of distinguishing occlusions and appearance changes according to their different risk possibilities. A new target template combining image intensity and histogram is designed. The corresponding updating method is also derived based on particle filter. If the target is totally occluded by another target, the template can be kept unchanged. The occlusion of a target will not influence tracking. Simulation results show that the presented method can efficiently justify whether the occlusion occurs and realize target tracking in image sequences even though the tracked target is totally occluded with long time.
The research is sponsored by 973 National Basic Research Program. Program No. 2006CB705700. Project name: Research on Key Scientific and technologic Problems of molecular imaging.
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References
Michael Acheson Isard Robotics Research Group: Visual Motion Analysis by Probabilistic Propagation of Conditional Density. PhD thesis (1998)
Wolfe, J.M.: Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review 1(2), 202–238 (1994)
Zhou, S., Chellappa, R., Moghaddam, B.: Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters. IEEE Transactions on Image Processing 13(11), 1491–1506 (2004)
Sanjeev Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing 50(2) (Feburary 2002)
Hu, W., Tan, T.: A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and reviews 34(3) (August 2004)
Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8) (August 1995)
Doucet, A., Godsil, S., Andrieu, C.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10, 197–208 (2000)
Morelande, M.R., Challa, S.: Manoeuvring target tracking in clutter using particle filters. IEEE Transactions on Aerospace and Electronic Systems 41(1), 252–270 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhou, Y., Hu, B., Zhang, J. (2006). Occlusion Detection and Tracking Method Based on Bayesian Decision Theory. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_47
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DOI: https://doi.org/10.1007/11949534_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
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