Foundations and Trends® in Signal Processing > Vol 3 > Issue 1–2

Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition

  • Chellappa, Rama 1
  • Sankaranarayanan, Aswin C. 2
  • Veeraraghavan, Ashok 3
  • Turaga, Pavan 4

[1]Chellappa, Rama, Department of Electrical and Computer Engineering, Center for Automation Research, UMIACS, at University of Maryland, rama@cfar.umd.edu [2]Sankaranarayanan, Aswin C., Department of Electrical and Computer Engineering, Rice University, saswin@rice.edu [3]Veeraraghavan, Ashok, Mistubishi Electric Research Laboratory, veerarag@merl.com [4]Turaga, Pavan, Department of Electrical and Computer Engineering, Center for Automation Research, UMIACS, at University of Maryland, pturaga@cfar.umd.edu

Published: 1 February 2010 © 2010 R. Chellappa, A. C. Sankaranarayanan, A. Veeraraghavan and P. Turaga

Short description

Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition highlights the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms.

Keywords

Image and video processing

Electrical and electronic engineering, Video and image processing and coding

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Table of contents

1 Introduction
2 Geometric Models for Imaging
3 Statistical Estimation Techniques
4 Detection, Tracking, and Recognition in Video
5 Statistical Analysis of Structure and Motion Algorithms
6 Shape, Identity, and Activity Recognition
7 Future Trends
Acknowledgments
References

Foundations and Trends® in Signal Processing

(Vol 3, Issue 1–2, 2009, pp 1-151)

DOI: 10.1561/2000000007

Abstract

Computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to interpreting scenes and recognizing objects, their structure and kinematics. In the presence of noisy observations and other uncertainties, computer vision algorithms make use of statistical methods for robust inference. In this monograph, we highlight the role of geometric constraints in statistical estimation methods, and how the interplay between geometry and statistics leads to the choice and design of algorithms for video-based tracking, modeling and recognition of objects. In particular, we illustrate the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and present appropriate statistical methods used in each of these problems.

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Table of contents

1: Introduction
2: Geometric Models for Imaging
3: Statistical Estimation Techniques
4: Detection, Tracking, and Recognition in Video
5: Statistical Analysis of Structure and Motion Algorithms
6: Shape, Identity and Activity Recognition
7: Future Trends
Acknowledgements
References
Cover image for Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition

Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition

160 pages

DOI: 10.1561/9781601983152

E-ISBN: 978-1-60198-315-2

ISBN: 978-1-60198-314-5

Description

Computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to solving some of these tasks. In the presence of noisy observations and other uncertainties, the algorithms make use of statistical methods for robust inference. Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition highlights the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms. In particular, it illustrates the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and appropriate statistical methods used in each of these problems