Incremental learning for Robust Visual Tracking

1. Incremental learning for Robust Visual Tracking 2013-10-08 KoDae-Won

2. Incremental learning for Robust Visual Tracking Contents • PCA • Face recognition for PCA • SequencialInference Model • Dynamical model • Observation model 6. Summary of the tracking algorithm

3. Incremental learning for Robust Visual Tracking 1. PCA(Principal Component Analysis)

4. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA X:

5. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA

6. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA 1. 2000 차원의 입력데이터를 그대로 사용하는것은계산량 의 증가 및 메모리 증가 2. PCA(Principal Component Analysis)를 이용해 차원 축소, 특징 추출 3. Covariance matrix ( ): 계산 4. (S: 150 X 150)

7. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA 7. 5. 6.

8. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA

9. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA

10. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA

11. Incremental learning for Robust Visual Tracking 2. Face recognition for PCA

12. Incremental learning for Robust Visual Tracking 3. Sequencial Inference Model Visual tracking problem : an inference task in a Markov model with hidden state variables

13. Incremental learning for Robust Visual Tracking 3. Sequencial Inference Model -Notations affine motion parameter -Probabilistic Formulation of Tracking Estimate

14. Incremental learning for Robust Visual Tracking 3. Sequencial Inference Model Given a set of observed image We aim to state the value of hidden state variable | ) | )|

15. Incremental learning for Robust Visual Tracking 4. Dynamical Model affine motion parameter : diagonal covariance matrix whose elements are the variances of affine parameter (i.e., ,,,,) =

16. Incremental learning for Robust Visual Tracking 4. Dynamical Model

17. Incremental learning for Robust Visual Tracking 5. Observation Model We model image observations using a probabilistic interpretation of PCA.

18. Incremental learning for Robust Visual Tracking 5. Observation Model Given an image patch predicated by We assume was generated from a subspace of the target Object spanned by U and centered at μ

19. Incremental learning for Robust Visual Tracking 5. Observation Model : mean, εI: +εI) ) +εI))

20. Incremental learning for Robust Visual Tracking 6. Summary of the tracking algorithm Summary of the tracking algorithm • Locate the target object in the first frame and use a single particle to indicate this location 2. Initialize the eigenbasis U to be empty, the mean to be the appearance of the target in the first frame.

21. Incremental learning for Robust Visual Tracking 6. Summary of the tracking algorithm 3. Advance to next frame. Draw particles from the particle filter, according to the dynamical model. 4. For each particle, extract the corresponding window, calculate its weights() 21

22. Incremental learning for Robust Visual Tracking 6. Summary of the tracking algorithm 5. Store the image window. When the desired number of new images have been accumulated, perform an incremental update of eigenbasis, mean and effective number of observations. 6. Go to step 3.

23. Incremental learning for Robust Visual Tracking 6. Summary of the tracking algorithm 5. Store the image window. When the desired number of new images have been accumulated, perform an incremental update of eigenbasis, mean and effective number of observations. 6. Go to step 3.