Ensemble Kalman Filter Variants for Multi-Object Tracking with False and Missing Measurements

  • Fabian SiggesEmail author
  • Marcus Baum
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 501)


In this chapter, we present an approach to Multi-Object Tracking (MOT) that is based on the Ensemble Kalman Filter (EnKF). The EnKF is a standard algorithm for data assimilation in high-dimensional state spaces that is mainly used in geosciences, but has so far only attracted little attention for object tracking problems. In our approach, the Optimal Subpattern Assignment (OSPA) distance is used for coping with unlabeled noisy measurements and a robust covariance estimation is done using FastMCD to deal with possible outliers due to false detections. A simple gating technique allows handling of missing detections. Additionally, a recently proposed JPDA variant of the EnKF is discussed. The filters are evaluated in two different scenarios with false detections, where a nearest neighbour Kalman Filter (NN-KF) serves as a baseline.


Ensemble Kalman Filter OSPA-metric False detections Missing detections Multi-Object Tracking 



This work was supported by the Simulation Science Center Clausthal-Göttingen.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of GoettingenGoettingenGermany

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