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Bayesian Kernel Tracking

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Book cover Pattern Recognition (DAGM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2449))

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Abstract

We present a Bayesian approach to real-time object tracking using nonparametric density estimation. The target model and candidates are represented by probability densities in the joint spatial-intensity domain. The new location and appearance of the target are jointly derived by computing the maximum likelihood estimate of the parameter vector that characterizes the transformation from the candidate to the model. This probabilistic formulation accommodates variations in the target appearance, while being robust to outliers represented by partial occlusions. In this paper we analyze the simplest parameterization represented by translation in both domains and present a gradient-based iterative solution. Various tracking sequences demonstrate the superior behavior of the method.

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Author information

Authors and Affiliations

  1. Real-Time Vision and Modeling Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ, 08540, USA

    Dorin Comaniciu

Authors
  1. Dorin Comaniciu
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Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, ETH Zürich, Gloriastrasse 35, 8092, Zürich, Switzerland

    Luc Van Gool

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© 2002 Springer-Verlag Berlin Heidelberg

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Comaniciu, D. (2002). Bayesian Kernel Tracking. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_53

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  • DOI: https://doi.org/10.1007/3-540-45783-6_53

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44209-7

  • Online ISBN: 978-3-540-45783-1

  • eBook Packages: Springer Book Archive

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