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Stochastic Algorithms for Visual Tracking

Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking

  • Book
  • © 2002

Overview

Authors:
  1. John MacCormick

    1. Systems Research Center, Compaq Computer Corporation, Palo Alto, USA

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Part of the book series: Distinguished Dissertations (DISTDISS)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction and background

    • John MacCormick
    Pages 1-7

  3. The Condensation algorithm

    • John MacCormick
    Pages 8-37

  4. Contour likelihoods

    • John MacCormick
    Pages 38-64

  5. Object localisation and tracking with contour likelihoods

    • John MacCormick
    Pages 65-91

  6. Modelling occlusions using the Markov likelihood

    • John MacCormick
    Pages 92-111

  7. A probabilistic exclusion principle for multiple objects

    • John MacCormick
    Pages 112-123

  8. Partitioned sampling

    • John MacCormick
    Pages 124-143

  9. Conclusion?

    • John MacCormick
    Pages 144-145

  10. Back Matter

    Pages 155-174
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Keywords

About this book

A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.

Authors and Affiliations

  • Systems Research Center, Compaq Computer Corporation, Palo Alto, USA

    John MacCormick

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