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Visual Tracking with Online Multiple Instance Learning

Visual Tracking with Online Multiple Instance Learning. 2014-02-04 Ko Daewon. Boris Babenko , Ming- Hsuan Yang, Serge Belongie CVPR 2009. Visual Tracking with Online Multiple Instance Learning. C ontent. Introduction Tracking by Detection Multiple Instance Learning (MIL)

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Visual Tracking with Online Multiple Instance Learning

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  1. Visual Tracking with Online Multiple Instance Learning 2014-02-04 KoDaewon • Boris Babenko, Ming-Hsuan Yang, Serge Belongie • CVPR 2009

  2. Visual Tracking with Online Multiple Instance Learning Content • Introduction • Tracking by Detection • Multiple Instance Learning (MIL) • Online MILboost • Experiments • Conclusion

  3. Visual Tracking with Online Multiple Instance Learning Introduction • Problem: Track a object in video in given location in first frame • Typical Tracking System: • Appearance Model • Color , subspaces, feature, etc • Optimization/Search • Greedy local search, etc

  4. Visual Tracking with Online Multiple Instance Learning System Overview : MILTrack

  5. Visual Tracking with Online Multiple Instance Learning Tracking by Detection • Recent tracking work • Focus on appearancemodel • Borrow techniques from object detection • Slide a discriminative classifier around image

  6. Visual Tracking with Online Multiple Instance Learning Tracking by Detection • Online AdaBoost : • Grab one positive patch, and some negative patch, and train/update the model. negative positive Classifier Online Classifier(Online AdaBoost )

  7. Visual Tracking with Online Multiple Instance Learning Tracking by Detection • An illustration of how most tracking by detection systems work Probablity map Frame t+1 Frame t Frame t+1 X X old location new location negative positive Model Model Step 1: Update Appearance Model Step 2: Update Appearance Model inside of window around old location Step 3: Update Tracker state

  8. Visual Tracking with Online Multiple Instance Learning Tracking by Detection • Repeat: negative positive negative positive Classifier Classifier

  9. Visual Tracking with Online Multiple Instance Learning Tracking by Detection • Problems: • What if classifier is a bit off? • Tracker starts to drift • How to choose training examples?

  10. Visual Tracking with Online Multiple Instance Learning Multiple Instance Learning(MIL) Instead of instance, get bagof instances Bag is positive if one or more of it’s members is positive A set of image patches :Positive :Negative

  11. Visual Tracking with Online Multiple Instance Learning Multiple Instance Learning(MIL) Updating a discriminative appearance model: (A) (B) (C) MIL Classifier Classifier Classifier

  12. Visual Tracking with Online Multiple Instance Learning Multiple Instance Learning(MIL) • MIL Training Input • The bag labels are defined as:

  13. Visual Tracking with Online Multiple Instance Learning Online MILBoost Framet Framet+1 Get data (bags) Update all M classifiers in pool Greedily add best K classifiersto strong classifier

  14. Visual Tracking with Online Multiple Instance Learning Boosting • Train classifier of the form: • where is a weak classifier • Can make binary predictions using

  15. Visual Tracking with Online Multiple Instance Learning Online MILBoost At tframe, Update all Mcandidate weak classifiers Pick best Kin a greedy fashion (M>K) …

  16. Visual Tracking with Online Multiple Instance Learning Online MILBoost • When the weak classifier receives new data • Use update rules: • The update rules for and are • similarly defined

  17. Visual Tracking with Online Multiple Instance Learning Online MILBoost • Objective to maximize: Log likelihood of bags: where： Noisy-OR Model, The bag probability The instance probability

  18. Visual Tracking with Online Multiple Instance Learning Online MILBoost M>K, M : total weak classifier candidates K : choosing the best K classifiers

  19. Visual Tracking with Online Multiple Instance Learning Online MILBoostvs Online AdaBoost

  20. Visual Tracking with Online Multiple Instance Learning Experiments • OAB:OnlineAdaBoost • SemiBoost:Online Semi-supervised Boosting • FragTrack= Stactic appearance model

  21. Visual Tracking with Online Multiple Instance Learning Experiments

  22. Visual Tracking with Online Multiple Instance Learning Experiments

  23. Visual Tracking with Online Multiple Instance Learning Conclusion • Present MILTrack that uses a novel Online Multiple Instance Learning algorithm • Using MIL to train an appearance model results in more robust tracking

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