A robustified hidden Markov model for visual tracking with subspace representation

Jiading Gai; Robert L. Stevenson

doi:10.1117/12.704596

29 January 2007 A robustified hidden Markov model for visual tracking with subspace representation

Jiading Gai, Robert L. Stevenson

Proceedings Volume 6508, Visual Communications and Image Processing 2007; 65080A (2007) https://doi.org/10.1117/12.704596
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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 {w_i}^t_i=1 to adjust the shape of the observation distribution. By carefully choosing the distribution of {w _i}^t_i=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.

Citation Download Citation

Jiading Gai and Robert L. Stevenson "A robustified hidden Markov model for visual tracking with subspace representation", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080A (29 January 2007); https://doi.org/10.1117/12.704596

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available