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Proceedings Paper

A robustified hidden Markov model for visual tracking with subspace representation
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Paper Abstract

This paper describes a new, robustified Hidden Markov Model for target tracking using a subspace representation. The Hidden Markov Model (HMM) provides a powerful framework for the probabilistic modelling of observations and states. Visual tracking problems are often cast as an inference problem within the HMM framework. Probabilistic Principal Component Analysis (PPCA), a classic subspace representation method, is a popular tool for appearance modelling because it provides a compact representation for high-dimensional data. Previous subspace based tracking algorithms assume the image observations were generated from a Gaussian distribution parameterized by principal components. One drawback of using Gaussian density model is that atypical observations cannot be modelled well. Hence, they are very sensitive to outliers. To address this problem, we propose to augment the HMM by adding a set of latent variables {wi}ti=1 to adjust the shape of the observation distribution. By carefully choosing the distribution of {w i}ti=1, we obtain a more robust observation distribution with heavier tails than a Gaussian. Numerical experiments demonstrate the effectiveness of this new framework in cases where the target objects are corrupted by noise or occlusion.

Paper Details

Date Published: 29 January 2007
PDF: 9 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080A (29 January 2007); doi: 10.1117/12.704596
Show Author Affiliations
Jiading Gai, Univ. of Notre Dame (United States)
Robert L. Stevenson, Univ. of Notre Dame (United States)

Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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