Robust Visual Tracking via Appearance Modeling and Sparse Representation

Abstract

When appearance variation of object, partial occlusion or illumination change in object images occurs, most existing tracking approaches fail to track the target effectively. To deal with the problem, this paper proposed a robust visual tracking method based on appearance modeling and sparse representation. The proposed method exploits two-dimensional principal component analysis (2DPCA) with sparse representation theory for constructing appearance model. Then tracking is achieved by Bayesian inference framework, in which a particle filter is applied to evaluate the target state sequentially over time. In addition, to make the observation model more robust, the incremental learning algorithm is used to update the template set. Both qualitative and quantitative evaluations on four publicly available benchmark video sequences demonstrate that the proposed visual tracking algorithm performs better than several state-of-the-art algorithms.

An Intelligent Visual Tracking System Based on Adaptive Mean Shift Tracking Method

“…Using patch-based appearance models [2], we can divide bounding box of a target into multiple patches and then selecting pertinent patches to construct appearance model. Due to this, we can obtain accurate tracking but it has more computational complexity than [4], in which online tracking involved and [7], in which 2D principal component analysis involves and in matter of solving whole object occlusion problem, this is not very efficient. This computational complexity decreases in [10], but here it is difficult to yield a sequence of closed form of updates.…”