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Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization

Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization. Authors: Baiyang Liu, Lin Yang, Junzhou Huang , Peter Meer, Leiguang Gong and Casimir Kulikowski. Outline. Problem of Tracking State of the art algorithms The proposed algorithm Experiment result. The problem.

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Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization

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  1. Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization Authors: Baiyang Liu, Lin Yang, Junzhou Huang, Peter Meer, Leiguang Gong and CasimirKulikowski

  2. Outline • Problem of Tracking • State of the art algorithms • The proposed algorithm • Experiment result

  3. The problem • Tracking: estimate the state of moving target in the observed video sequences • Challenges • Illumination, pose of target changes • Object occlusion, complex background clutters • Landmark ambiguity • Two categories of tracking • Discriminative • Generative

  4. Outline • Problem of Tracking • State of the art algorithms • The proposed algorithm • Experiment result

  5. Related work • Multiple Instance Learning boosting method(MIL Boosting) put all samples into bags and labeled them with bag labels. • Incremental Visual Tracking(IVT) the target is represented as a single online learned appearance model • L1 norm optimization a linear combination of the learned template set composed of both target templates and the trivial template.

  6. Basic sparse representation • Sparse representation • Basis pursuit • Disadvantages • Computationally expensive • Temporal and spatial features are not considered • The background pixels do not lie on the linear template subspace

  7. Outline • Problem of Tracking • State of the art algorithms • The proposed algorithm • Experiment result

  8. Problem Analysis • Given ,Let , , • Feature space can be decreased to K0 dimension • Two stage greedy method

  9. Stage I: Feature selection • Loss function Given , L= as labels, • To minimize the loss function, solve the sparse problem below • Feature selection matrix

  10. Stage II: Sparse reconstruction • Problem after stage I • Simplify the aim function above as

  11. Bayesian tracking framework • Let represents the affine paramters • Estimation of the state probability prediction: updating: • Transition model: ~ • likelihood where

  12. Review of the algorithm

  13. Outline • Problem of Tracking • State of the art algorithms • The proposed algorithm • Experiment result

  14. Visual results

  15. Quantitative results

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