Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function

Isaac Ronald Ward, M. A.Asim K. Jalwana, Mohammed Bennamoun

Research output: Chapter in Book/Conference paperConference paper

Abstract

This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique used when regressing a camera’s pose from an image is to formulate the loss as a linear combination of positional and rotational mean squared error (using tuned hyperparameters as coefficients). In this work we observe that changes to rotation and position mutually affect the captured image, and in order to improve performance, a pose regression network’s loss function should include a term which combines the error of both of these coupled quantities. Based on task specific observations and experimental tuning, we present said loss term, and create a new model by appending this loss term to the loss function of the pre-existing pose regression network ‘PoseNet’. We achieve improvements in the localization accuracy of the network for indoor scenes; with reductions of up to 26.7% and 24.0% in the median positional and rotational error respectively, when compared to the default PoseNet.

Original languageEnglish
Title of host publicationImage and Video Technology - PSIVT 2019 International Workshops, Revised Selected Papers
EditorsJoel Janek Dabrowski, Ashfaqur Rahman, Manoranjan Paul
PublisherSpringer Link
Pages111-124
Number of pages14
ISBN (Print)9783030397692
DOIs
Publication statusPublished - 2020
Event9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019 - Sydney, Australia
Duration: 18 Nov 201922 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11994 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2019
CountryAustralia
CitySydney
Period18/11/1922/11/19

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  • Cite this

    Ward, I. R., Jalwana, M. A. A. K., & Bennamoun, M. (2020). Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function. In J. J. Dabrowski, A. Rahman, & M. Paul (Eds.), Image and Video Technology - PSIVT 2019 International Workshops, Revised Selected Papers (pp. 111-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11994 LNCS). Springer Link. https://doi.org/10.1007/978-3-030-39770-8_9