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2D Person Tracking Using Kalman Filtering and Adaptive Background Learning in a Feedback Loop

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

Abstract

This paper proposes a system for tracking people in video streams, returning their body and head bounding boxes. The proposed system comprises a variation of Stauffer’s adaptive background algorithm with spacio-temporal adaptation of the learning parameters and a Kalman tracker in a feedback configuration. In the feed-forward path, the adaptive background module provides target evidence to the Kalman tracker. In the feedback path, the Kalman tracker adapts the learning parameters of the adaptive background module. The proposed feedback architecture is suitable for indoors and outdoors scenes with varying background and overcomes the problem of stationary targets fading into the background, commonly found in variations of Stauffer’s adaptive background algorithm.

Keywords

Kalman Filter Foreground Object Foreground Pixel Smart Space Shadow Detection 
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
  1. 1.Athens Information Technology, Autonomic and Grid Computing, P.O. Box 64, Markopoulou Ave., 19002 PeaniaGreece

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