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キーワード:
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要旨:
We present a method for the detection of instances of an
object class, such as cars or pedestrians, in natural images.
Similarly to some previous works, this is accomplished via
generalized Hough transform, where the detections of individual
object parts cast probabilistic votes for possible
locations of the centroid of the whole object; the detection
hypotheses then correspond to the maxima of the Hough
image that accumulates the votes from all parts. However,
whereas the previous methods detect object parts using generative
codebooks of part appearances, we take a more discriminative
approach to object part detection. Towards this
end, we train a class-specific Hough forest, which is a random
forest that directly maps the image patch appearance
to the probabilistic vote about the possible location of the
object centroid. We demonstrate that Hough forests improve
the results of the Hough-transform object detection significantly
and achieve state-of-the-art performance for several
classes and datasets.
