Krylov, Vladimir A and Kenny, Eamonn and Dahyot, Rozenn (2018) Automatic Discovery and Geotagging of Objects from Street View Imagery. Remote Sensing, 10 (5). pp. 1-20. ISSN 2072-4292

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Abstract

Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images, while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov random field model to estimate the objects’ geolocation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with the simultaneous presence of multiple, visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and position precision of approximately 2 m, which is approaching the precision of single-frequency GPS receivers.

Item Type:	Article
Keywords:	object geolocation; object mapping; street view imagery; Markov random fields; traffic lights; telecom assets; GPS estimation;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15105
Identification Number:	https://doi.org/10.3390/rs10050661
Depositing User:	Rozenn Dahyot
Date Deposited:	07 Dec 2021 16:20
Journal or Publication Title:	Remote Sensing
Publisher:	MDPI
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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