Object Detection

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Object Detection

• Using the Documented Viola-Jones Technique.
• Student Nathan Faggian, Supervisors Dr.
  Andrew Paplinski,
• Prof. Bala Srinivasan.

Version 1.1
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What is Object Detection?

• Detecting a specified object class within a
  image.
• Object detection has many applications in
  computer based vision.
• Object tracking, object recognition, scene
  surveillance.
• The focus of this project was to implement object
  detection, and to detect objects of the class
  face.

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How Is It Done?

• A standard pattern recognition problem.
• Feature extraction.
• Something that can be representative of a face.
• Feature evaluation.
• Does this something really represent a face.
• A bit of a black art
• Classification.
• Given a sample and its features, what is it?

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Common Techniques

• Strong focuses on statistics.
• Statistical models of images.
• Schneiderman-Kanade
• A lot of work with Neural networks.
• Generally slow systems.
• Rowley-Balauja
• Feature and Template methods seem to be the most
  common.

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Features of Good Techniques

• Quick to compute.
• Classification of a face does not require a lot
  of offline processing.
• Accurate.
• Most good implementations can provide accuracy
  above the 90 percentile.
• Capitalization on invariance.
• Features are invariant.
• Scale, luminance, rotation.

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Paul Viola and Michael Jones

• Devised a technique that was both robust and very
  quick.
• 15 times quicker than any technique at the time
  of release.
• A detection algorithm that could be operated in
  real-time.
• 95 accuracy at around 17fps.
• Understanding is the primary goal.
• It is a good technique!

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The Viola-Jones Technique

• Feature extraction and feature evaluation.
• Rectangular features are used, with a new image
  representation their calculation is very fast.
• Classifier training and feature selection using a
  method called AdaBoost.
• A long and exhaustive training process.
• A degenerate decision tree of classifiers is
  formed.
• The key to the techniques speed.

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Features

• Four basic types.
• They are easy to calculate.
• The white areas are subtracted from the black
  ones.
• A special representation of the sample called the
  integral image makes feature extraction faster.

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Integral images

• Summed area tables
• A representation that means any rectangles area
  can be calculated in four indexes to the integral
  image.

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Feature Extraction

• Features are extracted from sub windows of an
  sample image.
• The base size for a sub window is 24 by 24
  pixels.
• In a 24 pixel by 24 pixel sub window there are
  180,000 possible features to be calculated.
• What is the end result of feature extraction?
• A lot of data!
• This is called over fitting and the amount of
  data must be reduced.
• Overfitting can be compensated to an extent by
  logical elimination.

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Weak Classifiers

• A feature, a threshold and a parity.
• Thresholds are obtained by obtaining the mean
  value for the feature on both class sets and then
  averaging the two values.
• Parity defines the direction of the equality.

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Feature/Classifier Evaluation

• Using AdaBoost the number of features is
  dramatically reduced.
• A simple algorithm that selects one feature at a
  time and assigns weights to the feature.
  Producing a strong classifier.
• It is a method of selecting features but also
  able to train classifiers in a tree as well.
• Features are clustered together to form nodes in
  a degenerate decision tree.

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AdaBoost

• Given a sample set of images.
• For t 1T (rounds of boosting)
• A weak classifier is trained using a single
  feature.
• The error of the classifier is calculated.
• The classifier (or single feature) with the
  lowest error is selected, and combined with the
  priors to make a strong classifier.
• After a T rounds a T strong classifier is
  created.
• It is the weighted linear combination of the weak
  classifiers selected.

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Hard examples are isolated
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Classifier error is driven down.
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The Attentional Cascade

• Referred here as a degenerate decision tree.
• The reason the technique is fast.
• Quick rejection of sub windows.

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Motivation for a cascade

• Speed.
• Reduction of false positives.
• Each node is trained with the false positives of
  the prior.
• AdaBoost can be used in conjunction with a simple
  bootstrapping process to drive detection error
  down.
• Viola and Jones presented a method to do this,
  that iteratively builds boosted nodes, to a
  desired false positive rate.

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Implementations

• Two implementations were realized.
• Matlab based.
• Improved flexibility, able to produce quicker
  results.
• C system, more of a framework. (much faster)
• Far faster than the interpreted Matlab scripts
  yet less flexibility.

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Current Progress

• Attentional cascade training code is complete.
• Matlab/C framework for future work.
• Numerous monolithic detectors have been trained.

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An existing system

• OpenCV (intel)
• There is still much work
  to do!

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Questions?

• How was my talk?
• Can anything be explained better?
• Email nathan.faggian_at_mail.csse.monash.edu.au