Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors

@article{Eiselein2012RealTimeMT,
  title={Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors},
  author={Volker Eiselein and Daniel Arp and Michael P{\"a}tzold and Thomas Sikora},
  journal={2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance},
  year={2012},
  pages={325-330}
}
The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has recently attracted a lot of interest in the tracking community mainly for its linear complexity and its ability to deal with high clutter especially in radar/sonar scenarios. In the computer vision community however, underlying constraints are different from radar scenarios and have to be taken into account when using the PHD filter. In this article, we propose a new tree-based path extraction algorithm for… CONTINUE READING
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