NDLI: Automatic geographic metadata correction for sensor-rich video sequences

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Automatic geographic metadata correction for sensor-rich video sequences

Content Provider	ACM Digital Library
Author	Zimmermann, Roger Yin, Yifang Wang, Guanfeng
Abstract	Videos recorded with current mobile devices are increasingly geotagged at fine granularity and used in various location- based applications and services. However, raw sensor data collected is often noisy, resulting in subsequent inaccurate geospatial analysis. In this study, we focus on the challenging correction of compass readings and present an automatic approach to reduce these metadata errors. Given the small geo-distance between consecutive video frames, image-based localization does not work due to the high ambiguity in the depth reconstruction of the scene. As an alternative, we collect geographic context from OpenStreetMap and estimate the absolute viewing direction by comparing the image scene to world projections obtained with different external camera parameters. To design a comprehensive model, we further incorporate smooth approximation and feature-based rotation estimation when formulating the error terms. Experimental results show that our proposed pyramid-based method outperforms its competitors and reduces orientation errors by an average of 58.8%. Hence, for downstream applications, improved results can be obtained with these more accurate geo-metadata. To illustrate, we present the performance gain in landmark retrieval and tag suggestion by utilizing the accuracy-enhanced geo-metadata.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2997015
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	3d projection Geo-metadata correction Feature matching Geo-referenced video
Content Type	Text
Resource Type	Article

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