Simplified multitarget tracking using the phd filter for microscopic video data

  • Wood T
  • Yates C
  • Wilkinson D
 et al. 
  • 37


    Mendeley users who have this article in their library.
  • 26


    Citations of this article.


The probability hypothesis density (PHD) filter from the theory of random finite sets is a well-known method for multitarget tracking. We present the Gaussian mixture (GM) and improved sequential Monte Carlo implementations of the PHD filter for visual tracking. These implementations are shown to provide advantages over previous PHD filter implementations on visual data by removing complications such as clustering and data association and also having beneficial computational characteristics. The GM-PHD filter is deployed on microscopic visual data to extract trajectories of free-swimming bacteria in order to analyze their motion. Using this method, a significantly larger number of tracks are obtained than was previously possible. This permits calculation of reliable distributions for parameters of bacterial motion. The PHD filter output was tested by checking agreement with a careful manual analysis. A comparison between the PHD filter and alternative tracking methods was carried out using simulated data, demonstrating superior performance by the PHD filter in a range of realistic scenarios.

Author-supplied keywords

  • Bacterial motion
  • multitarget tracking
  • probability hypothesis density (PHD) filter
  • random finite sets
  • sequential Monte Carlo

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Gabriel RosserQueen Mary, University of London

  • Trevor M. Wood

  • Christian A. Yates

  • David A. Wilkinson

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free