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Research Article | Open Access

Rethinking random Hough Forests for video database indexing and pattern search

Multimedia and Vision Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
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Abstract

Hough Forests have demonstrated effective performance in object detection tasks, which has potential to translate to exciting opportunities in pattern search. However, current systems are incompatible with the scalability and performance requirements of an interactive visual search. In this paper, we pursue this potential by rethinking the method of Hough Forests training to devise a system that is synonymous with a database search index that can yield pattern search results in near real time. The system performs well on simple pattern detection, demonstrating the concept is sound. However, detection of patterns in complex and crowded street-scenes is more challenging. Some success is demonstrated in such videos, and we describe future work that will address some of the key questions arising from our work to date.

Keywords

Hough Forestspattern detectionpattern searchmachine learning

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Computational Visual Media
Volume 2 Issue 2,
June 2016
Pages 143-152
DOI: 10.1007/s41095-016-0039-3
Cite this article:
Henderson C, Izquierdo E. Rethinking random Hough Forests for video database indexing and pattern search. Computational Visual Media, 2016, 2(2): 143-152. https://doi.org/10.1007/s41095-016-0039-3

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Revised: 24 November 2015
Accepted: 18 December 2015
Published: 01 March 2016
© The Author(s) 2016

This article is published with open access at Springerlink.com

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