The AIT Outdoors Tracking System for Pedestrians and Vehicles

  • Aristodemos Pnevmatikakis
  • Lazaros Polymenakos
  • Vasileios Mylonakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)


This paper presents the tracking system from Athens Information Technology that participated to the pedestrian and vehicle surveillance task of the CLEAR 2006 evaluations. Two are the novelties of the proposed tracker. First, we use a variation of Stauffer’s adaptive background algorithm with spatiotemporal adaptation of the learning parameters and a Kalman filter in a feedback configuration. In the feed-forward path, the adaptive background module provides target evidence to the Kalman filter. In the feedback path, the Kalman filter adapts the learning parameters of the adaptive background module. Second, we combine a temporal persistence pixel map, together with edge information, to produce the evidence that is associated with targets. The proposed tracker performed well in the evaluations, and can be also applied to indoors settings and multi-camera tracking.


Kalman Filter Learning Rate Foreground Object Foreground Pixel Smart Space 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Aristodemos Pnevmatikakis
    • 1
  • Lazaros Polymenakos
    • 1
  • Vasileios Mylonakis
    • 1
  1. 1.Athens Information Technology, Autonomic and Grid Computing, P.O. Box 64, Markopoulou Ave., 19002 PeaniaGreece

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