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Tracking Multiple Objects Using the Condensation Algorithm

E. Koller-Meier and F. Ade
Journal of Robotics and Autonomous Systems
Vol. 34, pp. 93--105, February 2001


Some years ago a new tracker, the CONDENSATION algorithm, came to be known in the computer vision community. It describes a stochastic approach that has neither restrictions on the system and measurement models used nor on the distributions of the error sources, but it can not track an arbitrary, changing number of objects. In this paper an extension of the CONDENSATION algorithm is introduced that relies on a single probability distribution to describe the likely states of multiple objects. By introducing an initialization density, observations can flow directly into the tracking process, such that newly appearing objects can be handled.

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  author = {E. Koller-Meier and F. Ade},
  title = {Tracking Multiple Objects Using the Condensation Algorithm},
  journal = {Journal of Robotics and Autonomous Systems},
  year = {2001},
  month = {February},
  pages = {93--105},
  volume = {34},
  number = {},
  keywords = {Tracking, recursive Bayesian filter, Condensation algorithm, Monte Carlo filter, Kalman filter}