Abstract
Dealing with multi-object tracking in a particle filter raises several issues. A first essential point is to model possible interactions between objects. In this article, we represent these interactions using a fuzzy formalism, which allows us to easily model spatial constraints between objects, in a general and formal way. The second issue addressed in this work concerns the practical application of a multi-object tracking with a particle filter. To avoid a decrease of performances, a partitioned sampling method can be employed. However, to achieve good tracking performances, the estimation process requires to know the ordering sequence in which the objects are treated. This problem is solved by introducing, as a second contribution, a ranked partitioned sampling, which aims at estimating both the ordering sequence and the joint state of the objects. Finally, we show the benefit of our two contributions in comparison to classical approaches through two multi-object tracking experiments and the tracking of an articulated object.
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References
Khan, Z., Balch, T., Dellaert, F.: MCMC-Based particle filtering for tracking a variable number of interacting targets. IEEE Transactions Pattern Analysis and Machine Intelligence 27, 1805–1918 (2005)
Pham, D.: Fuzzy clustering with spatial constraints. In: IEEE International Conference on Image Processing, vol. II, pp. 65–68 (2002)
Colliot, O., Camara, O., Bloch, I.: Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation. Pattern Recognition 39, 1401–1414 (2006)
Fouquier, G., Atif, J., Bloch, I.: Local reasoning in fuzzy attribute graphs for optimizing sequential segmentation. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 138–147. Springer, Heidelberg (2007)
Doucet, A., Vo, B., Andrieu, C., Davy, M.: Particle filtering for multi-target tracking and sensor management. In: 5th International Conference on Information Fusion, pp. 474–481 (2002)
Schulz, D., Burgard, W., Fox, D., Cremers, A.: Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In: IEEE International Conference on Robotics & Automation, pp. 1665–1670 (2001)
Hue, C., Le Cadre, J.P., Prez, P.: Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transactions on Signal Processing 50, 309–325 (2002)
MacKay, D.J.C.: Introduction to Monte Carlo methods. In: Jordan, M.I. (ed.) Learning in Graphical Models. NATO Science Series, pp. 175–204. Kluwer Academic Press, Dordrecht (1998)
MacCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. International Journal of Computer Vision, 39, 57–71 (2000)
MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Smith, K., Gatica-Perez, D.: Order matters: A distributed sampling method for multi-object tracking. In: British Maschine Vision Conference, pp. 25–32 (2004)
Duffner, S., Odobez, J., Ricci, E.: Dynamic partitioned sampling for tracking with discriminative features. In: British Maschine Vision Conference (2009)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Information Sciences 8, 199–249 (1975)
Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, Inc., Orlando (1980)
Pérez, P., Vermaak, J.: Bayesian tracking with auxiliary discrete processes. application to detection and tracking of objects with occlusions. In: ICCV 2005 Workshop on Dynamical Vision, Beijing, China, pp. 190–202 (2005)
MacCormick, J., Blake, A.: Spatial dependence in the observation of visual contours. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 765–781. Springer, Heidelberg (1998)
Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 267–282 (2008)
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)
Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)
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Widynski, N., Dubuisson, S., Bloch, I. (2010). Introducing Fuzzy Spatial Constraints in a Ranked Partitioned Sampling for Multi-object Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_38
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DOI: https://doi.org/10.1007/978-3-642-17289-2_38
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