Artificial learning approaches for multi-target tracking.

• Author(s) / Creator(s)

  • McCrosky, Jesse
  • Kouritzin, Michael
  • Blount, Douglas

• A hybrid weighted/interacting particle filter, the selectively resampling particle (SERP) filter, is used to detect and track an unknown number of independent targets on a one-dimensional \"racetrack\" domain. The targets evolve in a nonlinear manner. The observations model a sensor positioned above the racetrack. The observation data takes the form of a discretized image of the racetrack, in which each discrete segment has a value depending both upon the presence or absence of targets in the corresponding portion of the domain, and upon lognormal noise. The SERP filter provides a conditional distribution approximated by particle simulations. After each observation is processed, the SERP filter selectively resamples its particles in a pairwise fashion, based on their relative likelihood. We consider a reinforcement learning approach to control this resampling. We compare two different ways of applying the filter to the problem: the signal measure approach and the model selection approach. We present quantitative results of the ability of the filter to detect and track the targets, for each of the techniques. Comparisons are made between the signal measure and model selection approaches, and between the dynamic and static resampling control techniques.

• Date created

  2004

• Subjects / Keywords

  • SERP filter
  • Non-linear filtering
  • Stochastic process
  • Particle filters
  • Neural networks
  • Artificial learning

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/R3SJ19V38

• License

  Copyright 2004 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.