Online Multiple Classifier Boosting
for Object Tracking
Tae-Kyun Kim1 Thomas Woodley1 Björn Stenger2 Roberto Cipolla1
1Dept.
of Engineering, University of Cambridge
2Computer Vision Group, Toshiba Research Europe
The Task: Object Tracking
Example sequence 1
Example sequence 2
Target appearance changes due to changes in
- pose
- illumination
- object deformation
Learning Multi-Modal Representations
Positive examples
Negative examples
- Multi-view face detection [Rowley et al. 98, Schneiderman et al. 00, Jones Viola 03]
- Multi-category detection, Sharing features [Torralba et al. 04]
Joint Clustering and Training
[Kim and Cipolla 08, Babenko et al. 08]
Positive examples
Feature pool
Negative examples
Face cluster 1
K-means
clustering
Face cluster 2
MCBoost: Multiple Strong Classifier Boosting
[Kim and Cipolla 08, Babenko et al. 08]
Given:
Set of n training samples
with labels
number of strong classifiers
Learn
strong classifiers:
Map to probabilities
with sigmoid function
Combine classifier output with
“Noisy OR” function
MCBoost (continued)
• 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: Toy Example 1
Input data
MCBoost result (K=3)
Toy Example 2
Standard AdaBoost
MCBoost
[Kim and Cipolla 08]
MC Boost with weighting function Q
MCBQ
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
MCBQ Algorithm
Input: Data set
, set of weak learners
, weighting function
Output: Strong classifiers
Init
with GMM
Init weights
to values of
for t=1,…,T // boosting rounds
for k=1,…,K // strong classifiers
Find weak learners
and their weights
Update sample weights
Update weighting function
end
end
MCBQ for Object Tracking
Principle:
1. (Short) supervised training phase
2. On-line updates
Online Boosting
[Oza, Russel 01,
Grabner, Bischof 06]
Global classifier pool
one
sample
Init
importance
Estimate
errors
Estimate
errors
Select best
weak
classifier
Update
weight
Estimate
importance
Select best
weak
classifier
Update
weight
Current strong classifier
Estimate
errors
Estimate
importance
Select best
weak
classifier
Update
weight
Online MCBQ
Classifiers
Sample weight
distribution
Select weak
classifiers, add
to
Selector
Selector
Selector
Update
Update weights,
re-normalize
Selector
Selector
Selector
Results
Improved Pose Expertise
MCBoost
MCBQ
Multi-pose Tracking with MCBQ
Tracking Experiments
Tracking “Cube” sequence
MILTrack
SemiBoost
MCBQ
Tracking Experiments
Tracking error
Summary
Extension of MCBoost to online setting
Extension of MIL to multi-class
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.
Thank you