CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Swarm intelligence
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Object Tracking with an Adaptive Color-Based Particle Filter
Proceedings of the 24th DAGM Symposium on Pattern Recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
Particle filtering with particle swarm optimization in systems with multiplicative noise
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Upper body tracking for human-machine interaction with a moving camera
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multiswarm particle filter for vision based SLAM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Efficient multi-feature PSO for fast gray level object-tracking
Applied Soft Computing
Ant Colony Estimator: An intelligent particle filter based on ACOR
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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A novel particle filter, enhancing particle swarm optimization based particle filter (EPSOPF), is proposed for visual tracking. Particle filter (PF) is sequential Monte Carlo simulation based on particle set representations of probability densities, which can be applied to visual tracking. However, PF has the impoverishment phenomenon which limits its application. To improve the performance of PF, particle swarm optimization with mutation operator is introduced to form new filtering, in which mutation operator maintain multiple modes of particle set and optimization-seeking procedure drives particles to their neighboring maximum of the posterior. When applied to visual tracking, the proposed approach can realize more efficient function than PF.