On detection of multiple object instances using hough transforms

Olga Barinova, Victor Lempitsky, Pushmeet Kholi

Research output: Contribution to journalArticlepeer-review

148 Citations (Scopus)

Abstract

Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.

Original languageEnglish
Article number6175905
Pages (from-to)1773-1784
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume34
Issue number9
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Hough transforms
  • line detection
  • object detection in images
  • scene understanding.

Fingerprint

Dive into the research topics of 'On detection of multiple object instances using hough transforms'. Together they form a unique fingerprint.

Cite this