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Robust Object Tracking with Online Multiple Instance Learning

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. Outline. Introduction

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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-HsuanYang, 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 appearancemodel • 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 Online classifier (i.e. Online AdaBoost) Classifier

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

  7. Tracking by Detection • Repeat… negative positive negative 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 bagof 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. Online MILBoost Framet Framet+1 Get data (bags) Update all M classifiers in pool Greedily add best Kto 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 tframe, Updateall Mcandidate classifiers • Pickbest Kin 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 MILBoostVS Online Adaboost

  17. System Overview: MILtrack

  18. Experiments • Compare MILTrackto: • 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 MILBoostalgorithm • Using MIL to train an appearance model results in more robust tracking

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