Robust False Positive Detection for Real-Time Multi-target Tracking

  • Henrik Brauer
  • Christos Grecos
  • Kai von Luck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8509)


We present a real-time multi-target tracking system that effectively deals with false positive detections. In order to achieve this, we build a novel motion model that treats false positives on background objects and false positives on foreground objects such as shoulders or bags separately. In addition we train a new head detector based on the Aggregated Channel Features (ACF) detector and propose a schema that includes the identification of true positives with the data association instead of using the internal decision-making process of the detector. Through several experiments, we show that our system is superior to previous work.


Data Association Foreground Object Town Centre False Positive Detection Ambient Assisted Living 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Henrik Brauer
    • 1
    • 2
  • Christos Grecos
    • 1
    • 2
  • Kai von Luck
    • 1
    • 2
  1. 1.University of the West of ScotlandUK
  2. 2.University of Applied Sciences HamburgGermany

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