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Online Multiple Classifier Boosting for Object Tracking
Tae-Kyun Kim1 Thomas Woodley1 Björn Stenger2 Roberto Cipolla1
1Dept. of Engineering, University of Cambridge2Computer Vision Group, Toshiba Research Europe
The Task: Object TrackingExample sequence 1
Target appearance changes due to changes in- pose - illumination- object deformation
Example sequence 2
Learning Multi-Modal Representations
- Multi-view face detection [Rowley et al. 98, Schneiderman et al. 00, Jones Viola 03]- Multi-category detection, Sharing features [Torralba et al. 04]
Positive examples
Negative examples
Joint Clustering and Training
K-means clustering
Face cluster 1
Face cluster 2
Positive examples Negative examplesFeature pool
[Kim and Cipolla 08, Babenko et al. 08]
Given:
Set of n training samples with labels number of strong classifiers
Learn strong classifiers:
Combine classifier output with“Noisy OR” function
Map to probabilitieswith sigmoid function
MCBoost: Multiple Strong Classifier Boosting[Kim and Cipolla 08, Babenko et al. 08]
• For given weights, find K weak-learners at t-th round of boosting to maximize
• Weak-learner weights found by a line search to maximize
where
• Sample weight update by AnyBoost method [Mason et al. 00]
MCBoost (continued)
MCBoost: Toy Example 1
Input data MCBoost result (K=3)
Toy Example 2
Standard AdaBoost
MCBoost [Kim and Cipolla 08]
MC Boost with weighting function QMC Boost with weighting function QMCBQ
Classifier Assignment
Make classifier assignment explicit using function
weight of strong classifier on sample
is updated at each round of boosting.
Here: K-component GMM in d-dim eigenspace, k-th mode is area of expertise of
Joint Boosting and Clustering
MCBoost MCBQ
Input: Data set , set of weak learnersOutput: Strong classifiers
for t=1,…,T // boosting roundsfor k=1,…,K // strong classifiers
Find weak learners and their weightsUpdate sample weights
endend
MCBQ Algorithm
Update sample weightsUpdate weighting function
Init with GMMInit weights to values of
, weighting function
MCBQ for Object TrackingPrinciple: 1. (Short) supervised training phase
2. On-line updates
Online Boosting
one sample
Init importance
Estimate errors
Select best weak classifier
Update weight
Estimate importance
Current strong classifier
[Oza, Russel 01, Grabner, Bischof 06]
Global classifier pool
Estimate errors
Select best weak classifier
Update weight
Estimate errors
Select best weak classifier
Update weight
Estimate importance
Online MCBQClassifiers
Sample weight distribution
Selector Selector Selector
Update
Selector Selector Selector
Select weak classifiers, add to
Update weights, re-normalize
Results
Improved Pose Expertise
MCBoost
MCBQ
Multi-pose Tracking with MCBQ
Tracking Experiments
Tracking “Cube” sequence
MCBQMILTrack SemiBoost
Tracking Experiments
Tracking error
Summary
Tracking: Build appearance model, then update online
No detector is required, i.e. not object specific.Handles rapid appearance changes.Simultaneous pose estimation and tracking is possible.
K is currently set by hand.Incorrect adaptation may still occur.
Extension of MCBoost to online settingExtension of MIL to multi-class
Thank you