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Issue No. 11 - November (2011 vol. 33)
ISSN: 0162-8828
pp: 2188-2202
Victor Lempitsky , University of Oxford, Oxford
Angela Yao , ETH Zurich, Zurich
Juergen Gall , ETH Zurich, Zurich
Nima Razavi , ETH Zurich, Zurich
Luc Van Gool , ETH Zurich, Zurich and IBBT, K.U., Leuven
The paper introduces Hough forests, which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a categorical level. At the same time, their flexibility permits extensions of the Hough transform to new domains such as object tracking and action recognition. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time. They achieve high detection accuracy since the entries of such codebooks are optimized to cast Hough votes with small variance and since their efficiency permits dense sampling of local image patches or video cuboids during detection. The efficacy of Hough forests for a set of computer vision tasks is validated through experiments on a large set of publicly available benchmark data sets and comparisons with the state-of-the-art.
Hough transform, object detection, tracking, action recognition.
Victor Lempitsky, Angela Yao, Juergen Gall, Nima Razavi, Luc Van Gool, "Hough Forests for Object Detection, Tracking, and Action Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 2188-2202, November 2011, doi:10.1109/TPAMI.2011.70
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