Ensemble_Tracking by niusheng11

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```									  Ensemble Tracking
Shai Avidan
IEEE TRANSACTIONS ON PATTERN ANALYSIS
AND MACHINE INTELLIGENCE
February 2007
outline

Introduction
Ensemble tracking
Implementation issues
Experiments

Resampling for Classifier Design
Bagging
Use multiple versions of a training set
• Each created by drawing n’<n samples from D with
replacement (i.e. if a sample is drawn, it is not removed
from D but is reconsidered in the next sampling)
• Each data set is used to train a different component
classifier
• The final classification decision is based on the vote of
the component classifiers

Boosting
To generate complementary classifiers by training
the next component classifier on the mistakes of
the previous ones
• Using a subset of the training data that is most
informative given the current set of component
classifiers
Adaboost trains a weak classifier on increasingly
more difficult examples and combines the result to
produce a strong classifier that is better than any of
the weak classifiers.
Weak classifier : hk ( x )  {1,1}
K m ax

Strong classifier : g ( x )    k hk ( x ) y  sign[ g ( x)]{1,1}
k 1

Use the same training set over and over
{( xi , yi ), i  1,.., n} yi  {1,1}
Each training pattern receives a weight Wk(i)
 The probability that the i-th pattern is drawn to take the kth
component classifier.
 Uniform initialization W1(i)=1/n
 If a training pattern is accurately classified hk(xi)=yi, its
chance of used again is reduced
 k        1 1  Et
Wk 1 (i )  Wk (i )  e           t  ln(     )
2    Et
 Otherwise, hk(xi)yi            Ek  training error measured on D using Wk (i )
Wk 1 (i )  Wk (i )  e k

Final decision
g ( x)    k hk ( x)
k
 Kmax component classifiers
hk ( x)  {1,1}
K m ax
g ( x)           h ( x)
k k
k 1                       y  sign[ g ( x)]{1,1}

{( xi , yi ), i  1,.., n}      yi  {1,1}
n
J   exp(  yi g ( xi )) h () ~ h ()
1      t 1
i 1
1 ~  t 1                  t 1
g t 1 ( x)    k hk ( x)
k 1
 At the t step
t
g t ( x)    k hk ( x)  g t 1 ( x)   t ht ( x)
k 1

n
J t   exp(  yi g t ( xi ))
i 1
n
  exp(  yi g t 1 ( xi )   t yi ht ( xi ))
i 1

n
  wt (i ) exp(  t yi ht ( xi ))
i 1
 Et e  t  (1  Et )e  t

J t
 Et e t  (1  Et )e  t  0
 t

Et e 2 t  1  Et

1 1  Et
 t  ln(     )
2    Et

wt 1 (i )  e  yi g t ( xi )  e  yi ( g t 1 ( xi )  t ht ( xi ))
 yi g t 1 ( xi )  yi t ht ( xi )                              yi t ht ( xi )
e                            e                           wt (i )e
Introduction

Considering tracking as a binary
classification problem.
Ensemble tracking as a method for
training classifiers on time-varying
distributions.
Ensemble of weak classifiers is trained
online to distinguish between the object
and the background.
Introduction
Introduction

Ensemble tracking maintains an implicit
representation of the foreground and the
foreground object explicitly alone.
Ensemble is not template-based methods.
Those maintains the spatial integrity of the
objects and are especially suited for
handling rigid objects.
Introduction

mean-shift tracking in a number of
important directions:
Mean-shift tracking usually works with
histogram of RGB colors. This is because gray-
scale images do not provide enough information
for tracking and high-dimensional feature
spaces cannot be modeled with histograms due
to exponential memory requirements.
Introduction

This is in contrast to existing methods that
either represent the foreground object
using the most recent histogram or some
ad hoc combination of the histograms of
the first and last frames.
Introduction

It breaks the time consuming training phase into
a sequence of simple and easy to compute
learning tasks that can be performed online.
It can also integrate offline and online learning
seamlessly.
Integrating classifier over time improves the
stability of the tracker in cases of partial
occlusions or illumination changes.
 In each frame, we keep the K “best” weak
classifiers, discard the remaining T-K new weak
classifiers, train T-K new weak classifiers on the
newly available data, and reconstruct the strong
weak classifier.
 The margin of the weak classifier h(x) is mapped
to a confidence measure c(x) by clipping
negative margins to zero and rescaling the
positive margins to the range [0,1].
Ensemble update
Ensemble tracking
During Step 7 of choosing the K best weak
classifier, weak classifiers do not perform
much better than chance.
We allow up to existing weak classifiers to
be removed this way because a large
number might be a sign of occlusion and
keep the ensemble unchanged for this
frame.
Implementations issues
 Outlier Rejection
Implementations issues
Implementations issues

Multiresolution Tracking
Implementations issues
experiments
 The first version uses five weak classifiers, each
working on an 11D feature vector per pixel that
consists of an 8-bin local histogram of oriented
gradients calculated on a 5x5 window as well as
the pixel R, G, and B valuse.
 To improve robustness, we only count edges
that are above some predefined threshold,
which war set to 10 intensity values.
experiments

 We found that the original feature space was not stable
enough and used a nonlinear version of that feature
[ xi , xi , xi ]

 We use only three, instead of five weak classifiers.
 Three levels of the pyramid
 In each frame, we drop one weak classifier and add a
newly trained weak classifier.
experiments

We allow the tracker to drop up to two
weak classifiers per frame because
dropping more than that might be could be
a sign of occlusion and we therefore do
not update the ensemble in such a case.
experiments
Results on Color Sequences: a pedestrian crossing the streat
experiments
Results on Color Sequences:
tracking a couple walking with a hand-held camera.
experiments
Results on Color Sequences:
tracking a face exhibiting out-of-plane rotations
experiments
Results on Color Sequences: tracking a red car that is undergoing out-of-
plane rotations and partial occlusions.
11D feature vector, single scale, an ensemble of three classifier was
enough to obtain robust and stable tracking
experiments
Analyze the importance of the update scheme for tracking:
experiments
Analyze how often are the weak classifiers updated?
experiments
Analyze how does their weight change over time.
experiments
Analyze how does this method compare with a standard AdaBoost
classifier that trains all its weak classifiers on a given frame?
experiments
Results on gray-scale sequence :
experiments
Results on IR sequence:
experiments
 Handling long-period occlusion
Classification rate is the fraction of the number pixels
that were correctly classified
As long as the classification rate is high, the tracking
goes unchanged.
When the classification level drops(<0.5), switch to
prediction mode.
Once occlusion is detected we start sampling, according
to the particle filter, possible location where the object
might appear.
In each such location, compute the classification score.
If it is above a threshold (0.7), then tracking resumes.
experiments
Handling occlusions:
experiments

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