A robustified hidden Markov model for visual tracking with subspace representation

摘要

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}_i~t=1 to adjust the shape of the observation distribution. By carefully choosing the distribution of {w_i}_i~t=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.