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Hough Forests for Object Detection, Tracking, and Action Recognition
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| Citations: | 97 - 23 self |
BibTeX
@MISC{Gall_houghforests,
author = {Juergen Gall and Angela Yao and Nima Razavi and Luc Van Gool and Victor Lempitsky},
title = {Hough Forests for Object Detection, Tracking, and Action Recognition},
year = {}
}
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Abstract
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 datasets and comparisons with the state-of-the-art.
Keyphrases
hough forest object detection action recognition hough transform local appearance fast matching available benchmark datasets computer vision task high detection accuracy hough vote task-adapted codebooks efficient way local image patch generalized hough transform allow fast categorical level random forest dense sampling new domain small variance test time object tracking large set video cuboid previous hough-based system implicit shape model
