On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Sequential Monte Carlo Methods to Train Neural Network Models
Neural Computation
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Recent advances and trends in visual tracking: A review
Neurocomputing
Robust object tracking for resource-limited hardware systems
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
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Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet. Inspired by the animal swarm intelligence in the evolutionary computing, we propose a swarm intelligence based particle filter algorithm. Unlike the independent particles in the conventional particle filter, the particles in our algorithm cooperate with each other and evolve according to the cognitive effect and social effect in analogy with the cooperative and social aspects of animal populations. Furthermore, the theoretical analysis shows that our algorithm is essentially a conventional particle filter with a hierarchial importance sampling process which is guided by the swarm intelligence extracted from the particle configuration, and thus greatly overcome the sample impoverishment problem suffered by particle filters. We compare the proposed approach with several nonlinear filters in the following tasks: state estimation, and visual tracking. The experiments demonstrate the effectiveness and promise of our approach.