Evaluation of Subpixel Tracking Algorithms

Evaluation of tracking algorithms can be done in several different ways, either using real or synthetic images. The main benefit with the second alternative is that the environment is completely controlled, there is no problem to get the ground truth and the noise is well known. This paper contains the results from an evaluation of subpixel tracking algorithms. The main focus of the evaluation is to compare the performance of subpixel methods with different computation complexity, in order to see whether the tracking performance justifies more complex algorithms.

Authors and Affiliations

1. Computer Vision Laboratory, Department of Electrical Engineering, Linköping University, SE-581 83, Linköping, Sweden

   Johan Skoglund & Michael Felsberg

Editors and Affiliations

1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

   George Bebis

2. NASA Ames Research Center, Moffett Field, CA, USA

   Richard Boyle

3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

   Bahram Parvin

4. Desert Research Institute, Reno, NV, USA

   Darko Koracin

5. Digital Image Research Center, Kingston University, London, UK

   Paolo Remagnino

6. Intel, 95052, Santa Clara, CA, USA

   Ara Nefian

7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

   Gopi Meenakshisundaram

8. Institute for Data Analysis and Visualization, P.O. Box

   Valerio Pascucci

9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

   Jiri Zara

10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

Reprints and permissions

© 2006 Springer-Verlag Berlin Heidelberg

Policies and ethics