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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

Visual Tracking with Online Multiple Instance Learning

2014-02-04

KoDaewon

  • Boris Babenko, Ming-Hsuan Yang, Serge Belongie
  • CVPR 2009
c ontent
Visual Tracking with Online Multiple Instance LearningContent
  • Introduction
  • Tracking by Detection
  • Multiple Instance Learning (MIL)
  • Online MILboost
  • Experiments
  • Conclusion
introduction
Visual Tracking with Online Multiple Instance LearningIntroduction
  • 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
tracking by detection
Visual Tracking with Online Multiple Instance LearningTracking by Detection
  • Recent tracking work
    • Focus on appearancemodel
    • Borrow techniques from object detection
      • Slide a discriminative classifier around image
tracking by detection1
Visual Tracking with Online Multiple Instance LearningTracking by Detection
  • Online AdaBoost :
  • Grab one positive patch, and some negative patch, and train/update the model.

negative

positive

Classifier

Online Classifier(Online AdaBoost )

tracking by detection2
Visual Tracking with Online Multiple Instance LearningTracking 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

tracking by detection3
Visual Tracking with Online Multiple Instance LearningTracking by Detection
  • Repeat:

negative

positive

negative

positive

Classifier

Classifier

tracking by detection4
Visual Tracking with Online Multiple Instance LearningTracking by Detection
  • Problems:
  • What if classifier is a bit off?
    • Tracker starts to drift
  • How to choose training examples?
multiple instance learning mil
Visual Tracking with Online Multiple Instance LearningMultiple 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

multiple instance learning mil1
Visual Tracking with Online Multiple Instance LearningMultiple Instance Learning(MIL)

Updating a discriminative appearance model:

(A)

(B)

(C)

MIL Classifier

Classifier

Classifier

online milboost
Visual Tracking with Online Multiple Instance LearningOnline MILBoost

Framet

Framet+1

Get data (bags)

Update all M classifiers

in pool

Greedily add best K classifiersto

strong classifier

boosting
Visual Tracking with Online Multiple Instance LearningBoosting
  • Train classifier of the form:
  • where is a weak classifier
  • Can make binary predictions using
online milboost1
Visual Tracking with Online Multiple Instance LearningOnline MILBoost

At tframe, Update all Mcandidate weak classifiers

Pick best Kin a greedy fashion (M>K)

online milboost2
Visual Tracking with Online Multiple Instance LearningOnline MILBoost
  • When the weak classifier receives new data
  • Use update rules:
  • The update rules for and are
  • similarly defined
online milboost3
Visual Tracking with Online Multiple Instance LearningOnline MILBoost
  • Objective to maximize: Log likelihood of bags:

where:

Noisy-OR Model, The bag probability

The instance probability

online milboost4
Visual Tracking with Online Multiple Instance LearningOnline MILBoost

M>K,

M : total weak

classifier candidates

K : choosing the

best K classifiers

experiments
Visual Tracking with Online Multiple Instance LearningExperiments
  • OAB:OnlineAdaBoost
  • SemiBoost:Online Semi-supervised Boosting
  • FragTrack= Stactic appearance model
conclusion
Visual Tracking with Online Multiple Instance LearningConclusion
  • 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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