Multi Target Tracking Using Determinantal Point Processes

  • Felipe Jorquera
  • Sergio Hernández
  • Diego Vergara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


Multi Target Tracking has many applications such as video surveillance and event recognition among others. In this paper, we present a multi object tracking (MOT) method based on point processes and random finite sets theory. The Probability Hypothesis Density (PHD) filter is a MOT algorithm that deals with missed, false and redundant detections. However, the PHD filter, as well as other conventional tracking-by-detection approaches, requires some sort of pre-processing technique such as non-maximum suppression (NMS) to eliminate redundant detections. In this paper, we show that using NMS is sub-optimal and therefore propose Determinantal Point Processes (DPP) to select the final set of detections based on quality and similarity terms. We conclude that PHD filter-DPP method outperforms PHD filter-NMS.


Multi object tracking Tracking-by-detection Determinantal Point Processes 



This work was supported by CONICYT/FONDECYT grant, project Robust Multi-Target Tracking using Discrete Visual Features, code 11140598.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Felipe Jorquera
    • 1
  • Sergio Hernández
    • 1
  • Diego Vergara
    • 1
  1. 1.Laboratorio GeoespacialUniversidad Católica del MauleTalcaChile

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