Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
This paper addresses human detection and
pose estimation from monocular images by formulating
it as a classification problem. Our main contribution is
a multi-class pose detector that uses the best components
of state-of-the-art classifiers including hierarchical
trees, cascades of rejectors as well as randomized
forests. Given a database of images with corresponding
human poses, we define a set of classes by discretizing
camera viewpoint and pose space. A bottom-up
approach is first followed to build a hierarchical tree
by recursively clustering and merging the classes at
each level. For each branch of this decision tree, we
take advantage of the alignment of training images to
build a list of potentially discriminative HOG (Histograms
of Orientated Gradients) features. We then select the
HOG blocks that show the best rejection performances.
We finally grow an ensemble of cascades by
randomly sampling one of these HOG-based rejectors at
each branch of the tree. The resulting multi-class
classifier is then used to scan images in a sliding window
scheme. One of the properties of our algorithm is that
the randomization can be applied on-line at no extra-cost,
therefore classifying each window with a different
ensemble of randomized cascades. Our approach, when
compared to other pose classifiers, gives fast and efficient
detection performances with both fixed and moving
cameras. We present results using different publicly
available training and testing data sets.
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Text Reference
Grégory Rogez, Jonathan Rihan, Carlos Orrite, and Philip H. S. Torr. Fast human pose detection using randomized hierarchical cascades of rejectors. International Journal of Computer Vision, 99(1):25–52, 2012.BibTeX Reference
@article{RogezROT_IJCV_2012,author = "Rogez, Gr{\'e}gory and Rihan, Jonathan and Orrite, Carlos and Torr, Philip H. S.",
title = "Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors",
journal = "International Journal of Computer Vision",
volume = "99",
number = "1",
year = "2012",
pages = "25-52",
ee = "http://dx.doi.org/10.1007/s11263-012-0516-9",
tag = "people"
}