Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization

  • Baiyang Liu
  • Lin Yang
  • Junzhou Huang
  • Peter Meer
  • Leiguang Gong
  • Casimir Kulikowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)


The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods.


Sparse Representation Reconstruction Error Appearance Model Visual Tracking Tracking Result 
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 2010

Authors and Affiliations

  • Baiyang Liu
    • 1
    • 2
  • Lin Yang
    • 2
  • Junzhou Huang
    • 1
  • Peter Meer
    • 3
  • Leiguang Gong
    • 4
  • Casimir Kulikowski
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityUSA
  2. 2.Deptartment of RadiologyUMDNJ-Robert Wood Johnson Medical SchoolUSA
  3. 3.Department of Electrical and Computer EngineeringRutgers UniversityUSA
  4. 4.IBM T.J. Watson ResearchUSA

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