A probabilistic integrated object recognition and tracking framework

  • Authors:

  • Francesc Serratosa;René Alquézar;Nicolás Amézquita

  • Affiliations:

  • Departament d'Enginyeria Informítica i Matemítiques, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain;Institut de Robòtica i Informítica Industrial, CSIC-UPC, Llorens Artigas 4-6, 08028 Barcelona, Spain;Departament d'Enginyeria Informítica i Matemítiques, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object.