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Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera

  • Wongun Choi
  • Silvio Savarese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

Tracking multiple objects is important in many application domains. We propose a novel algorithm for multi-object tracking that is capable of working under very challenging conditions such as minimal hardware equipment, uncalibrated monocular camera, occlusions and severe background clutter. To address this problem we propose a new method that jointly estimates object tracks, estimates corresponding 2D/3D temporal trajectories in the camera reference system as well as estimates the model parameters (pose, focal length, etc) within a coherent probabilistic formulation. Since our goal is to estimate stable and robust tracks that can be univocally associated to the object IDs, we propose to include in our formulation an interaction (attraction and repulsion) model that is able to model multiple 2D/3D trajectories in space-time and handle situations where objects occlude each other. We use a MCMC particle filtering algorithm for parameter inference and propose a solution that enables accurate and efficient tracking and camera model estimation. Qualitative and quantitative experimental results obtained using our own dataset and the publicly available ETH dataset shows very promising tracking and camera estimation results.

Keywords

Camera Motion Camera Parameter Camera Model Single Camera Structure From Motion 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wongun Choi
    • 1
  • Silvio Savarese
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of MichiganAnn ArborUSA

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