NDLI: System for semi-automated surveying of street-lighting poles from street-level panoramic images

Content Provider	IEEE Xplore Digital Library
Author	Hazelhoff, L. Creusen, I. de With, P.H.N.
Copyright Year	2014
Description	Author affiliation: Eindhoven Univ. of Technol., Eindhoven, Netherlands (de With, P.H.N.) \|\| CycloMedia Technol. B.V., Zaltbommel, Netherlands (Hazelhoff, L.; Creusen, I.)
Abstract	Accurate and up-to-date inventories of lighting poles are of interest to energy companies, beneficial for the transition to energy-efficient lighting and may contribute to a more adequate lighting of streets. This potentially improves social security and reduces crime and vandalism during nighttime. This paper describes a system for automated surveying of lighting poles from street-level panoramic images. The system consists of two independent detectors, focusing at the detection of the pole itself and at the detection of a specific lighting fixture type. Both follow the same approach, and start with detection of the feature of interest (pole or fixture) within the individual images, followed by a multi-view analysis to retrieve the real-world coordinates of the poles. Afterwards, the detection output of both algorithms is merged. Large-scale validations, covering about 135 km of road, show that over 91% of the lighting poles is found, while the precision remains above 50%. When applying this system in a semi-automated fashion, high-quality inventories can be created up to 5 times more efficiently compared to manually surveying all poles from the images.
Sponsorship	IEEE Comput. Soc.
Starting Page	129
Ending Page	136
File Size	10893840
Page Count	8
File Format	PDF
ISBN	9781479949854
DOI	10.1109/WACV.2014.6836109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lighting Detectors Image color analysis Roads Image edge detection Histograms Estimation
Content Type	Text
Resource Type	Article

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