Robust Object Tracking with Online Multiple Instance Learning

Advisor: Sheng-Jyh WangStudent: Pei Chu

Boris Babenko, Ming-Hsuan Yang, Serge Belongie. Robust Object Tracking with Online Multiple Instance Learning. IEEE Trans. on PAMI , 2011.

Outline

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

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]

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]

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)

Tracking by Detection

• Find max response

negative

positive old location

new location

XX

ClassifierClassifier

Tracking by Detection

• Repeat…

negative

positive

negative

positive

ClassifierClassifier

Problems

• What if classifier is a bit off?• Tracker starts to drift

• How to choose training examples?

Multiple Instance Learning (MIL)

• Instead of instance, get bag of instances• Bag is positive if one or more of it’s members is positive

[Keeler ‘90, Dietterich et al. ‘97] [Viola et al. ‘05]

Positive

Negative

Multiple Instance Learning (MIL)

• MIL Training Input

• The bag labels are defined as:

Online MILBoostFrame t Frame t+1

Get data (bags)

Update all M classifiersin pool

Greedily add best K tostrong classifier

Boosting

• Train classifier of the form:

where is a weak classifier• Can make binary predictions using

[Freund et al. ‘97]

Online MILBoost

• At t frame, Update all M candidate classifiers

• Pick best K in a greedy fashion (M>>K)

[Grabner et al. ‘06]

Online MILBoost

• Objective to maximize: Log likelihood of bags:

where：

[Viola et al. ’05, Friedman et al. ‘00]

Noisy-OR Model, The bag probability

The instance probability

Online MILBoost（ OMB）

M>K,

M :is total weak classifier candidates

K : is choosing the best K classifiers

Online MILBoost VS Online Adaboost

System Overview: MILtrack

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]

Results

Results

ResultsBestSecond Best

Conclusions

• Proposed Online MILBoost algorithm• Using MIL to train an appearance model results

in more robust tracking