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Visual Tracking Based on Log-Euclidean Riemannian Sparse Representation

  • Yi Wu
  • Haibin Ling
  • Erik Blasch
  • Li Bai
  • Genshe Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

Recently, sparse representation has been utilized in many computer vision tasks and adapted for visual tracking. Sparsity-based visual tracking is formulated as searching candidates with minimal reconstruction errors from a template subspace with sparsity constraints in the approximation coefficients. However, an intensity template is easily corrupted by noise and not robust for target tracking under a dynamic environment. The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. Further, the covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized, and its dimension is small. Although the covariance matrix lies on Riemannian manifolds, its log-transformation can be measured on a Euclidean subspace. Based on the covariance region descriptor and using the sparse representation, we propose a novel tracking approach on the Log-Euclidean Riemannian subspace. Specifically, the target region is characterized by a covariance matrix which is then log-transformed from the Riemannian manifold to the Euclidean subspace. After that, the target tracking problem is integrated under a sparse approximation framework, where the sparsity is achieved by solving an ℓ1-regularization problem. Then the candidate with the smallest approximation is taken as the tracked target. For target propagation, we use the Bayesian state inference framework, which propagates sample distributions over time using the particle filter algorithm. To evaluate our method, we have collected several video sequences and the experimental results show that our tracker can achieve robustly and reliably target tracking.

Keywords

Riemannian Manifold Sparse Representation Target Tracking Visual Tracking Covariance Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yi Wu
    • 1
  • Haibin Ling
    • 1
  • Erik Blasch
    • 2
  • Li Bai
    • 3
  • Genshe Chen
    • 1
  1. 1.Center for Data Analytics and Biomedical Informatics, Computer and Information Science DepartmentTemple UniversityPhiladelphiaUSA
  2. 2.Air Force Research Lab/SNAAUSA
  3. 3.Electrical and Computer Engineering DepartmentTemple UniversityPhiladelphiaUSA

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