Visual Tracking via Sparse Representation Based Linear Subspace Model

The modeling of the object appearance is one of the key issues in the development and application of effective object tracking. This paper presents a tracking algorithm based on representing the appearance of the object using a sparse representation based subspace model. the sparse representation theory offers us a powerful tool to model the object by only a small fraction of the training set. The multi-part subspace appearance model (MSAM) is learned via L¹-minimization and the Gramm-Schmidt process given enough training samples (overcomplete dictionary). Furthermore, a novel model updating strategy is designed to incrementally update the proposed subspace model and the dictionary. Finally, an observation model integrating both sparsity and the likelihood information is designed to embed the proposed modeling approach into the particle filter framework for efficient object tracking. Experimental results demonstrate the robustness and effectiveness of the algorithm, especially when the images are noisy or the objects exhibit large appearance changes.