"Rethinking random Hough Forests for video database indexing and patter" by Craig Henderson and Ebroul Izquierdo

Article Title

Rethinking random Hough Forests for video database indexing and pattern search

Authors

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

Keywords

Hough Forests, pattern detection, pattern search, machine learning

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.

DOI

https://doi.org/10.1007/s41095-016-0039-3

Publisher

Tsinghua University Press

Recommended Citation

Craig Henderson, Ebroul Izquierdo. Rethinking random Hough Forests for video database indexing and pattern search. Computational Visual Media 2016, 2(2): 143-152.

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