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Experimental Assessment of Probabilistic Integrated Object Recognition and Tracking Methods

  • Francesc Serratosa
  • Nicolás Amézquita
  • René Alquézar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

This paper presents a comparison of two classifiers that are used as a first step within a probabilistic object recognition and tracking framework called PIORT. This first step is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. One of the implemented classifiers is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results show that, on one hand, both classifiers (although they are very different approaches) yield a similar performance when they are integrated within the tracking framework. And on the other hand, our object recognition and tracking framework obtains good results when compared to other published tracking methods in video sequences taken with a moving camera and including total and partial occlusions of the tracked object.

Keywords

Object tracking object recognition occlusion performance evaluation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francesc Serratosa
    • 1
  • Nicolás Amézquita
    • 1
  • René Alquézar
    • 2
  1. 1.Universitat Rovira i VirgiliTarragonaSpain
  2. 2.Inst. Robòtica i Informàtica Industrial, CSIC-UPCBarcelonaSpain

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