Online Multiple Classifier Boosting for Object Tracking

1. Online Multiple Classifier Boosting for Object Tracking Tae-Kyun Kim1 Thomas Woodley1Björn Stenger2 Roberto Cipolla1 1Dept. of Engineering, University of Cambridge 2Computer Vision Group, Toshiba Research Europe

2. The Task: Object Tracking Example sequence 2 Example sequence 1 • Target appearance changes due to changes in • pose • illumination • object deformation

3. Learning Multi-Modal Representations Positive examples Negative examples - Multi-view face detection [Rowley et al. 98, Schneiderman et al. 00, Jones Viola 03] - Multi-category detection, Sharing features [Torralba et al. 04]

4. Joint Clustering and Training Feature pool [Kim and Cipolla 08, Babenko et al. 08] Negative examples Positive examples Face cluster 1 K-means clustering Face cluster 2

5. MCBoost: Multiple Strong Classifier Boosting [Kim and Cipolla 08, Babenko et al. 08] Given: Set of n training samples with labels number of strong classifiers Learn strong classifiers: Map to probabilities with sigmoid function Combine classifier output with “Noisy OR” function

6. MCBoost (continued) • For given weights, find K weak-learners at t-th round of boosting to maximize • Weak-learner weights found by a line search to maximize where • Sample weight update by AnyBoost method [Mason et al. 00]

7. MCBoost: Toy Example 1 Input data MCBoost result (K=3)

8. Toy Example 2

9. Standard AdaBoost

10. MCBoost [Kim and Cipolla 08]

11. MC Boost with weighting function Q MCBoost with weighting function Q MCBQ

12. Classifier Assignment Make classifier assignment explicit using function weight of strong classifier on sample is updated at each round of boosting. Here: K-component GMM in d-dim eigenspace, k-th mode is area of expertise of

13. Joint Boosting and Clustering MCBoost MCBQ

14. , weighting function Init with GMM Init weights to values of Update sample weights Update weighting function MCBQ Algorithm Input: Data set , set of weak learners Output: Strong classifiers for t=1,…,T // boosting rounds for k=1,…,K // strong classifiers Find weak learners and their weights Update sample weights end end

15. MCBQ for Object Tracking Principle: 1. (Short) supervised training phase 2. On-line updates

16. [Oza, Russel 01, Grabner, Bischof 06] Online Boosting Global classifier pool one sample Estimate errors Estimate errors Estimate errors Init importance Estimate importance Estimate importance Select best weak classifier Select best weak classifier Select best weak classifier Update weight Update weight Update weight Current strong classifier

17. Online MCBQ Classifiers Sample weight distribution Selector Selector Selector Select weak classifiers, add to Update Update weights, re-normalize Selector Selector Selector

18. Results

19. Improved Pose Expertise MCBoost MCBQ

20. Multi-pose Tracking with MCBQ

21. Tracking Experiments

22. Tracking “Cube” sequence MILTrack SemiBoost MCBQ

23. Tracking Experiments Tracking error

24. Summary Extension of MCBoost to online setting Extension of MIL to multi-class Tracking: Build appearance model, then update online No detector is required, i.e. not object specific. Handles rapid appearance changes. Simultaneous pose estimation and tracking is possible. K is currently set by hand. Incorrect adaptation may still occur.

25. Thank you