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Rapid Hand Detection with Adaboost Classifiers Based on Haar


									Hand Detection with a Cascade of Boosted
Classifiers Using Haar-like Features

    Qing Chen
    Discover Lab, SITE, University of Ottawa

    May 2, 2006
   1. Introduction
   2. Haar-like features
   3. Adaboost
   4. The Cascade of Classifiers
   5. Preliminary Results
   6. Future Work

1. Introduction
 Hand-based Human Computer Interface (HCI) should
  meet the requirements of real-time, accuracy and
 The purpose of Haar-like features is to meet the real-time
 The purpose of the cascade of Adaboosted (Adaptive
  boost) classifiers is to achieve both accuracy and speed.
 The algorithm has been used for face detection which
  achieved high detection accuracy and approximately 15
  times faster than any previous approaches.
 The algorithm is a generic objects detection/recognition

2. Haar-Like Features
   Each Haar-like feature consists of two or three jointed “black” and “white”

     Figure 1: A set of basic Haar-like features.

                                                    Figure 2: A set of extended Haar-like features.

   The value of a Haar-like feature is the difference between the sum of the
    pixel gray level values within the black and white rectangular regions:
       f(x)=Sumblack rectangle (pixel gray level) – Sumwhite rectangle (pixel gray level)
   Compared with raw pixel values, Haar-like features can reduce/increase
    the in-class/out-of-class variability, and thus making classification easier.

2. Haar-Like Features (cont’d)
 The rectangle Haar-like features can be computed rapidly using
  “integral image”.
 Integral image at location of x, y contains the sum of the pixel
  values above and left of x, y, inclusive:

                 P( x, y)         i ( x' , y ' )
                              x ' x , y ' y                              P (x, y)

 The sum of pixel values within “D”:                         A        B
                                                                  P1        P2
   P  A, P2  A  B, P3  A  C, P4  A  B  C  D
    1                                                         C        D

   P  P4  P2  P3  A  A  B  C  D  A  B  A  C  D
    1                                                             P3        P4

2. Haar-Like Features (cont’d)
   To detect the hand, the image is scanned by a sub-window containing a
    Haar-like feature.

   Based on each Haar-like feature fj , a weak classifier hj(x) is defined as:

    where x is a sub-window, and θ is a threshold. pj indicating the direction
    of the inequality sign.
3. Adaboost
   The computation cost using Haar-like features:
    Example: original image size: 320X240,
             sub-window size: 24X24,
             frame rate: 15 frame/second,
    The total number of sub-windows with one Haar-like feature per second:


    Considering the scaling factor and the total number of Haar-like features,
    the computation cost is huge.
   AdaBoost (Adaptive Boost) is an iterative learning algorithm to construct
    a “strong” classifier using only a training set and a “weak” learning
    algorithm. A “weak” classifier with the minimum classification error is
    selected by the learning algorithm at each iteration.
   AdaBoost is adaptive in the sense that later classifiers are tuned up in
    favor of those sub-windows misclassified by previous classifiers.

3. Adaboost (cont’d)
   The algorithm:

3. Adaboost (cont’d)
 Adaboost      starts  with    a   uniform
distribution of “weights” over training
examples. The weights tell the learning
algorithm the importance of the example.

 Obtain a weak classifier from the weak
learning algorithm, hj(x).

 Increase the weights on the training
examples that were misclassified.

 (Repeat)

 At the end, carefully make a linear
combination of the weak classifiers
obtained at all iterations.

  f final (x)   final ,1h1 (x)     final ,n hn (x)
4. The Cascade of Classifiers
   A series of classifiers are applied to every sub-window.
   The first classifier eliminates a large number of negative sub-windows and pass
    almost all positive sub-windows (high false positive rate) with very little
   Subsequent layers eliminate additional negatives sub-windows (passed by the
    first classifier) but require more computation.
   After several stages of processing the number of negative sub-windows have
    been reduced radically.

4. The Cascade of Classifiers (cont’d)
   Negative samples: non-object
    images. Negative samples are
    taken from arbitrary images.
    These images must not contain
    object representations.

   Positive samples: images contain
    object (hand in our case). The
    hand in the positive samples must
    be marked out for classifier

5. Preliminary Results

   Number of pos. samples: 144
   Number of neg. samples: 3142
   Sample Resolution: 640X480
   Initial sub-window size: 15X30
   Scale factor: 1.3
   Cascade obtained: 12 grades

6. Future Work
   Extended Haar-like features? Will
    extended Haar-like features improve
    the detection accuracy? (Still an Open
    Problem) The performance tradeoff?
   Parallel cascades for multiple hand
    gestures. How to select the hand
    gesture configurations which can be
    detected more effectively with the
    employed Haar-like feature set?
   Improve the robustness against hand
   How much improvement can be
    achieved with more training samples?
    Intel face detection classifier: 5000 Pos.
    10000 Neg. Accuracy: 98%

   Wu Bo, et al., “A Multi-View Face Detection Based on Real Adaboost Algorithm,” Computer
    Research and Development, 42 (9):pp.1612-1621,2005.
   Paul Viola and Michael J. Jones, “Robust Real-time Object Detection,” Technical Report,
    Cambridge Research Lab, Compaq. 2001.
   Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, “Analysis of Boosting Algorithms
    using the Smooth Margin Function: A Study of Three Algorithms,” 2004.
   Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky, “Empirical Analysis of Detection
    Cascades of Boosted Classifiers for Rapid Object Detection,” MRL Technical Report, May
   Andre L. C. Barczak, Farhad Dadgostar, “Real-time Hand Tracking Using a Set of
    Cooperative Classifiers and Haar-Like Features,” Research Letters in the Information and
    Mathematical Sciences, ISSN 1175-2777, Vol. 7, pp 29-42, 2005.
   Mathias Kölsch and Matthew Turk, “Robust Hand Detection,” Proc. IEEE Intl. Conference on
    Automatic Face and Gesture Recognition, May 2004.
   Intel OpenCV Documents.
   Acknowledgement goes to Urtho’s training data for eye detection and F. Dadgostar’s hand
    palm database.

Thank you and Any Questions?


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