Latent-Class Hough Forests for 3D object detection and pose estimation of rigid objects

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Title: Latent-Class Hough Forests for 3D object detection and pose estimation of rigid objects
Author(s): Tejani, Alykhan
Item Type: Thesis or dissertation
Abstract: In this thesis we propose a novel framework, Latent-Class Hough Forests, for the problem of 3D object detection and pose estimation in heavily cluttered and occluded scenes. Firstly, we adapt the state-of-the-art template-based representation, LINEMOD [34, 36], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. In training, rather than explicitly collecting representative negative samples, our method is trained on positive samples only and we treat the class distributions at the leaf nodes as latent variables. During the inference process we iteratively update these distributions, providing accurate estimation of background clutter and foreground occlusions and thus a better detection rate. Furthermore, as a by-product, the latent class distributions can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected a new, more challenging, dataset for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We evaluate the Latent-Class Hough Forest on both of these datasets where we outperform state-of-the art methods.
Content Version: Open Access
Publication Date: Jul-2014
Date Awarded: Dec-2014
Advisor: Stathaki, Tania
Sponsor/Funder: Engineering and Physical Sciences Research Council
Department: Electrical and Electronic Engineering
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Master of Philosophy (MPhil)
Appears in Collections:Electrical and Electronic Engineering PhD theses

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