Robust object tracking based on sparse representation In this paper, we propose a novel and robust object tracking algorithm based on sparse representation. Object tracking is formulated as a object recognition problem rather than a traditional search problem. All target candidates are considered as training samples and the target template is represented as a linear combination of all training samples. The combination coefficients are obtained by solving for the minimum l 1 norm solution. The final tracking result is the target candidate associated with the nonzero coefficient. Experimental results on two challenging test sequences show that the proposed method is more effective than the widely used mean shift tracker.

A robustified hidden markov model for visual tracking with subspace representation 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 highdimensional 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.