Spider monkey optimisation assisted particle filter for robust object tracking

@article{Rohilla2017SpiderMO,
  title={Spider monkey optimisation assisted particle filter for robust object tracking},
  author={Rajesh Rohilla and Vanshaj Sikri and Rajiv Kapoor},
  journal={IET Comput. Vis.},
  year={2017},
  volume={11},
  pages={207-219}
}
Particle filters (PFs) are sequential Monte Carlo methods that use particle representation of state-space model to implement the recursive Bayesian filter for non-linear and non-Gaussian systems. Owing to this property, PFs have been extensively used for object tracking in recent years. Although PFs provide a robust object tracking framework, they suffer from shortcomings. Particle degeneracy and particle impoverishment brought by the resampling step result in abysmal construction of posterior… 
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