Abstract
In this paper, we propose a particle filtering approach for tracking applications in image sequences. The system we propose combines a measurement equation and a dynamic equation which both depend on the image sequence. Taking into account several possible observations, the likelihood is modeled as a linear combination of Gaussian laws. Such a model allows inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter. It also enables building a relevant approximation of a validation gate. We demonstrate the significance of this model for a point tracking application.
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Anderson, B.D.O., Moore, J.B.: Optimal Filtering, Englewood Cliffs (1979)
Arnaud, E., Mémin, E., Cernuschi-Frías, B.: A robust stochastic filter for point tracking in image sequences. In: ACCV (2004)
Arnaud, E., Mémin, E., Cernuschi-Frías, B.: Conditional filters for image sequence based tracking - application to point tracker. Proc. IEEE trans. on Im. (2004) (accepted for publication)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. TSP 50(2) (2002)
Aschwanden, P., Guggenbühl, W.: Experimental results from a comparative study on correlation-type registration algorithms. In: Förstner, W., Ruwiedel, S. (eds.) Robust Computer Vision, pp. 268–289 (1992)
Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, London (1988)
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)
Breidt, F.J., Carriquiry, A.L.: Highest density gates for target traking. IEEE Trans. on Aerospace and Electronic Systems 36(1), 47–55 (2000)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to non-linear/non-Gaussian Bayesian state estimation. IEEE Processing-F (Radar and Signal Processing) 140(2) (1993)
Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)
Kong, A., Liu, J.S., Wong, W.H.: Sequential imputations and Bayesian missing data problems. Journal of the American Statistical Association 89(425), 278–288 (1994)
Meyer, F., Bouthemy, P.: Region-based tracking using affine motion models in long image sequences. CVGIP:IU 60(2), 119–140 (1994)
Odobez, J.-M., Bouthemy, P.: Robust multiresolution estimation of parametric motion models. Journ. of Vis. Com. and Im. Repr. 6(4), 348–365 (1995)
Papanikolopoulos, N.P., Khosla, P.K., Kanade, T.: Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision. IEEE Trans. on Robotics and Automation 9(1), 14–35 (1993)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)
Singh, A., Allen, P.: Image-flow computation: An estimation-theoric framework and a unified perspective. CVGIP: IU 56(2), 152–177 (1992)
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Arnaud, E., Mémin, E. (2004). Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24672-5_24
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DOI: https://doi.org/10.1007/978-3-540-24672-5_24
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