Introducing Fuzzy Spatial Constraints in a Ranked Partitioned Sampling for Multi-Object Tracking

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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Submitted on : Sunday, May 15, 2011 - 6:24:10 PM
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Nicolas Widynski, Séverine Dubuisson, Isabelle Bloch. Introducing Fuzzy Spatial Constraints in a Ranked Partitioned Sampling for Multi-Object Tracking. 6th International Symposium on Visual Computing (ISVC'10), Nov 2010, Las Vegas, Nevada, United States. pp.393-404, ⟨10.1007/978-3-642-17289-2_38⟩. ⟨hal-00593419⟩

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