Dbn-based combinatorial resampling for articulated object tracking

Severine Dubuisson 1 Christophe Gonzales 2 Xuan Son Nguyen 2
1 PEQUAN - Performance et Qualité des Algorithmes Numériques
LIP6 - Laboratoire d'Informatique de Paris 6
2 DECISION
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Particle Filter is an effective solution to track objects in video sequences in complex situations. Its key idea is to estimate the density over the possible states of the object using a weighted sample whose elements are called particles. One of its crucial step is a resampling step in which particles are resampled to avoid some degeneracy problem. In this paper, we introduce a new resampling method called Combinatorial Resampling that exploits some features of articulated objects to resample over an implicitly created sample of an exponential size better representing the density to estimate. We prove that it is sound and, through experimentations both on challenging synthetic and real video sequences, we show that it outperforms all classical resampling methods both in terms of the quality of its results and in terms of response times
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Severine Dubuisson, Christophe Gonzales, Xuan Son Nguyen. Dbn-based combinatorial resampling for articulated object tracking. Conference on Uncertainty in Artificial Intelligence (UAI'12), Aug 2012, Catalina Island, United States. pp.237-246. ⟨hal-00821797⟩

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