Localised mixture models in region-based tracking

authored by: Christian Schmaltz, Bodo Rosenhahn, Thomas Brox, Joachim Weickert
Abstract: An important problem in many computer vision tasks is the separation of an object from its background. One common strategy is to estimate appearance models of the object and background region. However, if the appearance is spatially varying, simple homogeneous models are often inaccurate. Gaussian mixture models can take multimodal distributions into account, yet they still neglect the positional information. In this paper, we propose localised mixture models (LMMs) and evaluate this idea in the scope of model-based tracking by automatically partitioning the fore- and background into several subregions. In contrast to background subtraction methods, this approach also allows for moving backgrounds. Experiments with a rigid object and the HumanEva-II benchmark show that tracking is remarkably stabilised by the new model.
Organisation(s): Institute of Information Processing
External Organisation(s): Saarland University
University of California at Berkeley
Type: Conference contribution
Pages: 21-30
No. of pages: 10
Publication date: 2009
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Theoretical Computer Science, Computer Science(all)
Electronic version(s): https://doi.org/10.1007/978-3-642-03798-6_3 (Access: Unknown)

BibTeX

@inproceedings{7cce2e1bab5b417a8e617053da64ab33,
title = "Localised mixture models in region-based tracking",
abstract = "An important problem in many computer vision tasks is the separation of an object from its background. One common strategy is to estimate appearance models of the object and background region. However, if the appearance is spatially varying, simple homogeneous models are often inaccurate. Gaussian mixture models can take multimodal distributions into account, yet they still neglect the positional information. In this paper, we propose localised mixture models (LMMs) and evaluate this idea in the scope of model-based tracking by automatically partitioning the fore- and background into several subregions. In contrast to background subtraction methods, this approach also allows for moving backgrounds. Experiments with a rigid object and the HumanEva-II benchmark show that tracking is remarkably stabilised by the new model.",
author = "Christian Schmaltz and Bodo Rosenhahn and Thomas Brox and Joachim Weickert",
note = "Funding information: We gratefully acknowledge funding by the German Research Foundation (DFG) under the project We 2602/5-1.; 31st Annual Symposium of the Deutsche Arbeitsgemeinschaft fur Mustererkennung, DAGM 2009 ; Conference date: 09-09-2009 Through 11-09-2009",
year = "2009",
doi = "10.1007/978-3-642-03798-6_3",
language = "English",
isbn = "3642037976",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "21--30",
booktitle = "Pattern Recognition",
}