Hough forests for object detection, tracking, and action recognition

Juergen Gall, Angela Yao, Nima Razavi, Luc Van Gool, Victor Lempitsky

Research output: Contribution to journalArticlepeer-review

489 Citations (Scopus)

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 data sets and comparisons with the state-of-the-art.

Original languageEnglish
Article number5740927
Pages (from-to)2188-2202
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume33
Issue number11
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • action recognition
  • Hough transform
  • object detection
  • tracking

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