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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)

Abstract

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.

Keywords

Data Association Foreground Object Town Centre False Positive Detection Ambient Assisted Living 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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