We propose an original particle filtering-based approach combining optimization and decomposition techniques for sequential non-parametric density estimation defined in high-dimensional state spaces. Our method relies on Annealing to focus on the correct distributions and on probabilistic conditional independences defined by Dynamic Bayesian Networks to focus samples on their modes. After proving its theoretical correctness and showing its complexity, we highlight its ability to track single and multiple articulated objects both on synthetic and real video sequences. We show that our approach is particularly effective, both in terms of estimation errors and computation times.