An Experimental Framework for Evaluating PTZ Tracking Algorithms

  • Pietro Salvagnini
  • Marco Cristani
  • Alessio Del Bue
  • Vittorio Murino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)


PTZ (Pan-Tilt-Zoom) cameras are powerful devices in video surveillance applications, because they offer both wide area coverage and highly detailed images in a single device. Tracking with a PTZ camera is a closed loop procedure that involves computer vision algorithms and control strategies, both crucial in developing an effective working system. In this work, we propose a novel experimental framework that allows to evaluate image tracking algorithms in controlled and repeatable scenarios, combining the PTZ camera with a calibrated projector screen on which we can play different tracking situations. We applied such setup to compare two different tracking algorithms, a kernel-based (mean-shift) tracking and a particle filter, opportunely tuned to fit with a PTZ camera. As shown in the experiments, our system allows to finely investigate pros and cons of each algorithm.


Particle Filter Tracking Algorithm Visual Tracking Intrinsic Parameter Optical Center 
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 2011

Authors and Affiliations

  • Pietro Salvagnini
    • 1
  • Marco Cristani
    • 1
    • 2
  • Alessio Del Bue
    • 1
  • Vittorio Murino
    • 1
    • 2
  1. 1.Istituto Italiano di Tecnologia (IIT)GenovaItaly
  2. 2.Computer Science DepartmentUniversity of VeronaVeronaItaly

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