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Robust Object Tracking with Online Multiple Instance Learning

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1 Robust Object Tracking with Online Multiple Instance Learning
Boris Babenko, Ming-Hsuan Yang, Serge Belongie. Robust Object Tracking with Online Multiple Instance Learning. IEEE Trans. on PAMI , 2011. Advisor: Sheng-Jyh Wang Student: Pei Chu

2 Outline Introduction Tracking by Detection（Related Work）
Multiple Instance Learning (MIL) Online MILboost Experiments Conclusion

3 Introduction: Tracking
Problem: track arbitrary object in video given location in first frame Typical Tracking System: Appearance Model Color , subspaces, feature,etc Optimization/Search Greedy local search, etc [Ross et al. ‘07]

4 Tracking by Detection Recent tracking work Focus on appearance model
Borrow techniques from object detection Slide a discriminative classifier around image [Collins et al. ‘05, Grabner et al. ’06, Ross et al. ‘08]

5 Tracking by Detection: Online AdaBoost
Grab one positive patch, and some negative patch, and train/update the model. negative positive Classifier Online classifier (i.e. Online AdaBoost)

6 Tracking by Detection Find max response negative positive old location
new location X X Classifier Classifier

7 Tracking by Detection Repeat… negative negative positive positive
Classifier Classifier

8 Problems What if classifier is a bit off? Tracker starts to drift
How to choose training examples?

9 Multiple Instance Learning (MIL)
Instead of instance, get bag of instances Bag is positive if one or more of it’s members is positive Positive Negative [Keeler ‘90, Dietterich et al. ‘97] [Viola et al. ‘05]

10 Multiple Instance Learning (MIL)
MIL Training Input The bag labels are defined as:

11 Update all M classifiers
Online MILBoost Frame t Frame t+1 Get data (bags) Update all M classifiers in pool Greedily add best K to strong classifier

12 Boosting Train classifier of the form: where is a weak classifier
Can make binary predictions using [Freund et al. ‘97]

13 Online MILBoost At t frame, Update all M candidate classifiers
Pick best K in a greedy fashion (M>>K) [Grabner et al. ‘06]

14 Online MILBoost Objective to maximize: Log likelihood of bags: where：
Noisy-OR Model, The bag probability The instance probability [Viola et al. ’05, Friedman et al. ‘00]

15 Online MILBoost（OMB） M>K, M :is total weak classifier candidates
K : is choosing the best K classifiers

16 Online MILBoost VS Online Adaboost

17 System Overview: MILtrack

18 Experiments Compare MILTrack to:
OAB1 = Online AdaBoost w/ 1 pos. per frame OAB5 = Online AdaBoost w/ 45 pos. per frame SemiBoost = Online Semi-supervised Boosting FragTrack = Static appearance model [Grabner ‘06, Adam ‘06, Grabner ’08]

19 Results

20 Results

21 Results Best Second Best

22 Conclusions Proposed Online MILBoost algorithm
Using MIL to train an appearance model results in more robust tracking

23

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