research-article

Outdoor Landmark Detection for Real-World Localization using Faster R-CNN

Authors:

Sivapong Nilwong,

Delowar Hossain,

Shin-ichiro Kaneko,

Genci CapiAuthors Info & Claims

ICCMA 2018: Proceedings of the 6th International Conference on Control, Mechatronics and Automation

Pages 165 - 169

https://doi.org/10.1145/3284516.3284532

Published: 12 October 2018 Publication History

Abstract

This paper presents a method for outdoor localization using deep learning-based landmark detection. The proposed localization method relies on the Faster Regional Convolutional Neural Network (Faster R-CNN) landmark detector and the feedforward neural network (FFNN) trained with GPS data from geotags in images, retrieve location coordinates and compass orientation of the implemented device based on detected landmarks in the image. Results of the proposed localization method are illustrated with errors from the comparisons between results of the localization and geotags data within the images. The experiment results pointed the proposed method to be the promising alternative to conventional ways of outdoor localization.

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Cited By

Paiva SAmaral AGonçalves JLima RBarreto L(2022)Image Recognition-Based Architecture to Enhance Inclusive Mobility of Visually Impaired People in Smart and Urban EnvironmentsSustainability10.3390/su14181156714:18(11567)Online publication date: 15-Sep-2022
https://doi.org/10.3390/su141811567
Nassar ALefevre SWegner J(2019)Simultaneous Multi-View Instance Detection With Learned Geometric Soft-Constraints2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00666(6558-6567)Online publication date: Oct-2019
https://doi.org/10.1109/ICCV.2019.00666

Index Terms

Outdoor Landmark Detection for Real-World Localization using Faster R-CNN
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
    2. Knowledge representation and reasoning
      1. Cognitive robotics

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cover image ACM Other conferences

ICCMA 2018: Proceedings of the 6th International Conference on Control, Mechatronics and Automation

October 2018

198 pages

ISBN:9781450365635

DOI:10.1145/3284516

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SFedU: Southern Federal University
University of Alberta: University of Alberta

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2018

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ICCMA 2018: 2018 The 6th International Conference on Control, Mechatronics and Automation

October 12 - 14, 2018

Tokyo, Japan

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Cited By

Paiva SAmaral AGonçalves JLima RBarreto L(2022)Image Recognition-Based Architecture to Enhance Inclusive Mobility of Visually Impaired People in Smart and Urban EnvironmentsSustainability10.3390/su14181156714:18(11567)Online publication date: 15-Sep-2022
https://doi.org/10.3390/su141811567
Nassar ALefevre SWegner J(2019)Simultaneous Multi-View Instance Detection With Learned Geometric Soft-Constraints2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00666(6558-6567)Online publication date: Oct-2019
https://doi.org/10.1109/ICCV.2019.00666

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