DBN-Based Combinatorial Resampling for Articulated Object Tracking

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.

Efficient human situation recognition using Sequential Monte Carlo in discrete state spaces

This dissertation analyses these challenges and provides solutions for SMC methods. The large, categorical and causal state-space is the largest factor for the inefficiency of current SMC methods. The marginal filter is analysed in detail for its advantages in categorical states over the particle filter. An optimal pruning strategy for the marginal filter is derived that limits the number of samples.Diese Dissertation analysiert diese Herausforderungen und entwickelt Lösungen für SMC-Methoden. Der große, kategorische und kausale Zustandsraum ist der größte Faktor für die Ineffizienz von aktuellen SMC-Methoden. Die Vorteile des Marginalen Filters in kategorischen Zustandsräumen gegenüber dem Partikelfilter werden detailliert analysiert. Eine optimale Pruning-Strategie wird für den Marginal Filter entwickelt