1. Incremental Boosting
Incremental Learning of Boosted Face Detector ICCV 2007
Unsupervised Incremental Learning for Improved Object Detection in a Video CVPR 2012
Jeany Son

2. Outline Supervised incremental learning for boosted classifier
Incremental Learning of Boosted Face Detector, ICCV 2007
Unsupervised incremental learning for boosted classifier
Unsupervised Incremental Learning for Improved Object Detection in a Video, CVPR 2012

3. Incremental Learning for Boosted Face Detector
ICCV 2007

4. Face Detection - Hard examples

5. Incremental Learning
Offline Learning
vs.
Incremental Learning - +

6. Domain-partitioned Real Adaboost
is minimized when
where

7. Loss function for incremental learning
Domain-partitioning Strong classifier
Likelihood of incremental learning: Linear combination of offline & online parts
Minimize upper bound on the training error by minimizing Z

8. Key issues
1) Adjustable parameters of the strong classifier H(x) 2) Estimation of Loff(H(x)) without offline samples 3) Choice of linear combination coefficient αy

9. Adjustable Parameters

10. F(x) is learned by means of some discriminative criterion
e.g. KL divergence, Bhattacharyya distance
Obtain proper domain partition of instance space for discrimination of different categories
Small online samples  to adjust F(x) is not unreasonable
Offline training : determine F(x)
Incremental training : adjust G(z)

11. Estimating Offline Loss function (without offline samples)
Naïve Bayes

12. Online Loss function & Optimization
Minimize loss using Steepest-decent method

13. Linear Combination Coefficient ( α 𝑦 )
Online reinforcement ratio of category y
Contributions of online/offline sample

14. Datasets
False alarm that is learned incrementally

16. Comparisons in CMU+MIT frontal face dataset
False alarm that is learned incrementally

18. Unsupervised Incremental Learning for Boosted Detector
CVPR 2012

19. Unsupervised Incremental MIL
Crowded environment and cluttered background

20. Unsupervised Incremental MIL
Contribution
MIL based incremental learning for Real Adaboost
Unsupervised online sample collection

21. Unsupervised Incremental MIL
Offline
detector
Tracker
Online sample collection
Incremental
Learning
Positive samples
Negative samples
Missing or
Low confidence
False alarm

22. Online Sample Collection
Unmerged detection responses : detection responses obtained from all the scanning windows for a given video frame
Merged detection responses : obtained using hierarchical clustering over all the unmerged detection responses
Track these detection responses to obtain the tracks T l = T 1 , T 2 ,…, 𝑇 𝑚
[C.Huang, B.Wu, R.Nevatia, Robust object tracking by hierarchical association of detection responses, In ECCV 2008]
Prune tracks which are less than ½ second or less than 10% of detection responses of track are confident

24. Online Sample Collection
Positive Samples : missing or low confidence detection
Negative Samples : False alarm
Positive bag : consist of 10 patches around a missed detection
Negative bag : one unmerged false alarm
(30% overlap of detection with track response)

25. MIL loss function
Soft max/min
soft loss function

27. Overfitting Avoidance
Inc1:base=offline detector
Inc0:base=previous iteration

28. Detection results
False alarm
Missing object

29. Detection results
False alarm
Missing object