CoTraining and its Uses - PowerPoint PPT Presentation


PPT – CoTraining and its Uses PowerPoint presentation | free to view - id: 38453-OTQ3Y


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation

CoTraining and its Uses


Want to detect cars on a road, used to count # of cars that pass an area ... 50 labeled samples used. Put cameras in 3 areas and let the online update run for each ... – PowerPoint PPT presentation

Number of Views:65
Avg rating:3.0/5.0
Slides: 17
Provided by: shaneb6
Tags: cotraining | cars | online | used | uses


Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: CoTraining and its Uses

Co-Training and its Uses
  • Unsupervised Improvement of Visual Detectors
    using Co-Training (ICCV '03)?
  • A. Levin, P. Viola, Y. Freund
  • Hebrew Univ. MSR Columbia Univ.
  • Online Detection and Classification of Moving
    Objects Using Progressively Improving Detectors
    (CVPR '05)?
  • Omar Javed, Saad Ali, Mubarak Shah
  • University of Central Florida

  • Classifiers need labeled data, but
  • data is expensive. So,
  • Have classifiers teach each other!

Co-Training Visualized
Levin, Viola, Freund's Work Idea
  • Want to detect cars on a road, used to count of
    cars that pass an area
  • Build classifiers on training set, find threshold
    above which all samples correctly labeled
  • On the unlabeled data, any sample which falls
    above the threshold is considered labeled and
    passed to the other classifier

Feature and Classifier Types
  • Appearance information is encoded using a
    gray-level histogram
  • Camera is stationary, use background subtraction
    to find motion regions. These regions serve as
    the other feature (ie is there motion, and what
    does it look like)
  • A LogAdaBoost (Collins) classifier is trained for
    each using 50 positive samples
  • 22,000 unlabeled samples are also fed in and
    incorporated using co-training

Co-Training for Detection
  • After initial train, find ?p and ?n. Label some
    unlabled data, find new ?p and ?n, repeat
  • Are many possible negative samples, pick those
    closest to ?n
  • For a pos sample, number of windows have high
    score, only pick the local max
  • Let it run loose on some data! Performance won't
    be higher than previous methods, improvement is
    in small of labeled data

Results (kinda)?
  • ROC for Gray-level feature (left) and motion
    feature (right). Green lines are before
    co-training. Black lines are after
  • Gray-level and background subtraction detection
    results before and after co-training

UC Florida's Work The Idea
  • Train an initial detector. But this detector cant
    achieve its maximum performance if its too
  • Perform an online update of the classifier.
    Classifier will learn to perform well in its
    specific environment

Feature and Classifier Types
  • Perform PCA, keep m largest eigenvectors. Do this
    for pedestrian and for vehicle data
  • Feature vector is rTSmp, rTSmv where r is the
    sample (in column-vector form, size dx1 where d
    of pixels) and S is the dxm eigenvector matrix
  • Construct a Bayes classifier for each coefficient
    of the feature vector

How Co-Training is Used
  • Classifiers are trained using AdaBoost, where the
    base classifier is the Bayes classifiers
  • Find the ?p and ?n, label the samples. Make more
    conservative by enforing that 1/10 of base
    classifiers must confidently label a sample in
    order to add it to the classifier
  • Only keep negative samples close to ?n

  • 50 labeled samples used. Put cameras in 3 areas
    and let the online update run for each

Co-Tracking! (is awesome)?
  • Start with a few labeled frames
  • Build an initial classifier, one for each feature
  • In the first unlabeled frame, use your
    classifiers to find the object
  • Each classifier gets a vote to the final tracking

Feature and Classifier Types
  • Color Histogram often very discriminative, but
    performs poorly over illumination changes
  • Histogram of Gradients (HoG) often not as
    discriminative, but (mostly) invariant to
    illumination changes
  • Features are complimentary. And usually at least
    one will find the object

How Co-Training is Used
  • After classifiers vote and a final location is
    found this frame is labeled. Add the object
    location as positive sample if confidence is
  • Need negative samples, but tradeoff of certainty
    vs. usefulness
  • Find BG regions HoG did poorly, pass them as
    negative samples to color classifier, and vice

Thank You!