Rao-Blackwellised Particle Filter with Adaptive System Noise and its Evaluation for Tracking in Surveillance

  title={Rao-Blackwellised Particle Filter with Adaptive System Noise and its Evaluation for Tracking in Surveillance},
  author={Xinyu Xu and Baoxin Li},
In the visual tracking domain, Particle Filtering (PF) can become quite inefficient when being applied into high dimensional state space. Rao-Blackwellisation [1] has been shown to be an effective method to reduce the size of the state space by marginalizing out some of the variables analytically . In this paper based on our previous work [3] we propose an RBPF tracking algorithm with adaptive system noise model. Experiments using both simulation data and real data show that the proposed RBPF… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 15 references

Mutsumoto, “Reconstructions and predictions of nonlinear dynamical systems by Rao- Blackwellised sequential Monte Carlo

  • T. Soma, T. K. Yosui
  • IEEE International Conference on Acoustics…
  • 2003

People Tracking with Anonymous and Id-sensors Using Rao-Blackwellised Particle Filters

  • D. Schulz, D. Fox, J. Hightower
  • in Intl. Joint Conf. on Artificial Intelligence…
  • 2003

A tutorial on Particle Filters for On-line Non-linear/Non- Gaussian Bayesian Tracking

  • S.Arulampalam, S.Maskell, N.Gordon, T. Clapp
  • IEEE Transactions of Signal Processing,
  • 2002

Comparison of the Particle Filter with Range Parameterized and Modified Polar ekf for Angle-Only Tracking

  • S. Arulampalam, B. Ristic
  • Signal and Data Processing of Small Targets,
  • 2000

Similar Papers

Loading similar papers…