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

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
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

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

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

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

13. Review of the algorithm

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

15. Visual results

16. Quantitative results