Geolocating Static Cameras

Jacobs, Nathan; Satkin, Scott; Roman, N.; Speyer, Richard; Pless, Robert

doi:10.1109/iccv.2007.4408995

Cited by 98 publications

(63 citation statements)

References 10 publications

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“…Positional Accuracy and Credibility Jacobs et al (2007) explored the varying positional accuracy of photos by matching photos with ancillary satellite imagery. They localize cameras based on satellite imagery that correlates with the camera images taken at a known time.…”

Section: Quality Assessment In Image-based Vgimentioning

confidence: 99%

A review of volunteered geographic information quality assessment methods

Senaratne

Mobasheri

Ali

et al. 2016

International Journal of Geographical Information Science

392

315

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With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. The phenomena is known as Volunteered Geographic Information (VGI). During the last decade VGI has been used as a data source supporting a wide range of services such as environmental monitoring, events reporting, human movement analysis, disaster management etc. However, these volunteer contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this paper, we review various quality measures and indicators for selected types of VGI, and existing quality assessment methods. As an outcome, the paper presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings we introduce data mining as an additional approach for quality handling in VGI.

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Section: Quality Assessment In Image-based Vgimentioning

confidence: 99%

A review of volunteered geographic information quality assessment methods

Senaratne

Mobasheri

Ali

et al. 2016

International Journal of Geographical Information Science

392

315

View full text Add to dashboard Cite

show abstract

“…There are some researches about geographical location determination. Jacobs, Satkin, Roman, Speyer and Pless (2007, October) find that a camera can be geo-located by the time course of principal component coefficients without determining correspondences between cameras or explicitly reasoning about weather in the scene. Junejo and Foroosh (2008) present a novel application where, using only three points from the shadow trajectory of the objects, the geo-location of the camera can be determined accurately, and also the date of image can be acquired without using any GPS or other special instruments.…”

Section: Introductionmentioning

confidence: 99%

Geographical Location Estimation based on An Improved Particle Swarm Optimization

Chen¹,

Deng²,

Zhang³

2016

APR

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This study presents a strategy for determining the relatively accurate geographical location of an object based on a video or a sequence of images taken at regular intervals. On the basis of a series of solar formulas, a precise object's shadow length model that includes the latitude and longitude of the photographer is built to describe how the shadow changes. Inputting the shadow length of the object and the creation time of the image into the built model, parameters like the latitude or longitude can be estimated by Particle Swarm Optimization (PSO). To solve the problem that PSO is easy to get stuck into local optima, a compression factor and mutation operation are introduced to the algorithm. Through analyzing the instance, the improved PSO algorithm has demonstrated itself with enhancement in convergence and accuracy. In conclusion, the improved PSO is an effective and precise tool to estimate the geographical location of video or images.

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“…Our approach takes inspiration from recent works in cross-view data retrieval that tackle problems such as static cameras localization with satellite imagery [11], cross-view action recognition [16] and image-text retrieval [19,23]. These approaches achieve cross-view retrieval by learning the co-occurrence of features in different views.…”

Section: Introductionmentioning

confidence: 99%

Cross-View Image Geo-localization

Lin¹,

Belongie²,

Hays³

2016

Large-Scale Visual Geo-Localization

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The recent availability of large amounts of geotagged imagery has inspired a number of data driven solutions to the image geolocalization problem. Existing approaches predict the location of a query image by matching it to a database of georeferenced photographs. While there are many geotagged images available on photo sharing and street view sites, most are clustered around landmarks and urban areas. The vast majority of the Earth's land area has no ground level reference photos available, which limits the applicability of all existing image geolocalization methods. On the other hand, there is no shortage of visual and geographic data that densely covers the Earthwe examine overhead imagery and land cover survey data -but the relationship between this data and ground level query photographs is complex. In this paper, we introduce a cross-view feature translation approach to greatly extend the reach of image geolocalization methods. We can often localize a query even if it has no corresponding groundlevel images in the database. A key idea is to learn the relationship between ground level appearance and overhead appearance and land cover attributes from sparsely available geotagged ground-level images. We perform experiments over a 1600 km 2 region containing a variety of scenes and land cover types. For each query, our algorithm produces a probability density over the region of interest.

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Geolocating Static Cameras

Cited by 98 publications

References 10 publications

A review of volunteered geographic information quality assessment methods

A review of volunteered geographic information quality assessment methods

Geographical Location Estimation based on An Improved Particle Swarm Optimization

Cross-View Image Geo-localization

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