A Simple Content-Based Strategy for Estimating the Geographical Location of a Webcam

Sandnes, Frode Eika

doi:10.1007/978-3-642-15702-8_4

Cited by 5 publications

(10 citation statements)

References 15 publications

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“…Then, as the accuracy of the sun elevation measurements decreased the largest error for this dataset was 16.9°l atitude and 12.8°longitude. These results are superior to those obtained using image intensity [25] and matches the accuracy obtained using webcam image sequences [28].…”

Section: Geographical Accuracysupporting

confidence: 83%

“…Another branch of related research attempts to determine the geographical location of webcams [23,26,28]. Webcams are often used to acquire sequences of regularly spaced images for monitoring purposes.…”

Section: Webcam Measurementsmentioning

confidence: 99%

“…Webcam image sequences have been used to determine the relative position of webcams and their orientation [23,24]. Moreover, an accuracy of about 2°was achieved using a contents-based intensity measure of webcam images sampled every 5 to 11 min [28]. This approach allowed the sunrise and sunsets to be determined, and hence the solar noon and length of day could be calculated.…”

Section: Webcam Measurementsmentioning

confidence: 99%

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Determining the Geographical Location of Image Scenes based on Object Shadow Lengths

Sandnes

2010

J Sign Process Syst

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Many studies have addressed various applications of geo-spatial image tagging such as image retrieval, image organisation and browsing. Geo-spatial image tagging can be done manually or automatically with GPS enabled cameras that allow the current position of the photographer to be incorporated into the meta-data of an image. However, current GPS-equipment needs certain time to lock onto navigation satellites and these are therefore not suitable for spontaneous photography. Moreover, GPS units are still costly, energy hungry and not common in most digital cameras on sale. This study explores the potential of, and limitations associated with, extracting geo-spatial information from the image contents. The elevation of the sun is estimated indirectly from the contents of image collections by measuring the relative length of objects and their shadows in image scenes. The observed sun elevation and the creation time of the image is input into a celestial model to estimate the approximate geographical location of the photographer. The strategy is demonstrated on a set of manually measured photographs.

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Section: Geographical Accuracysupporting

confidence: 83%

Section: Webcam Measurementsmentioning

confidence: 99%

Section: Webcam Measurementsmentioning

confidence: 99%

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Determining the Geographical Location of Image Scenes based on Object Shadow Lengths

Sandnes

2010

J Sign Process Syst

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“…The sunrise and sunset were determined by classifying images taken from a webcam and the location was then estimated in [11]. For determining the sunrise and sunset, the intensity of the image was used to classify day or night and then determine the midday (or local noon) time to identify the longitude and latitude.…”

Section: Introductionmentioning

confidence: 99%

Image-based geographical location estimation using web cameras

Choe

Pramoun

Amornraksa

et al. 2014

2014 Southwest Symposium on Image Analysis and Interpretation

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This paper describes a method for estimating the location of an IP-connected camera (a web cam) by analyzing a sequence of images obtained from the camera. First, we classify each image as Day/Night using the mean luminance of the sky region. From the Day/Night images, we estimate the sunrise/set, the length of the day, and local noon. Finally, the geographical location (latitude and longitude) of the camera is estimated. The experiment results show that our approach achieves reasonable performance.

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“…Wu and Cao (2010) investigate the property of solar shadow trajectories on a planar surface and shows that camera parameters, latitude, longitude can be estimated from two observed shadow trajectories. Sandnes (2010) proposes a strategy for determining the approximate geographical location of a webcam based on a time-stamped image sequence spanning 24 hours. Based on the sunrise and sunset times which can be acquired from the images, both the latitude and longitude of the webcam can be determined.…”

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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A Simple Content-Based Strategy for Estimating the Geographical Location of a Webcam

Cited by 5 publications

References 15 publications

Determining the Geographical Location of Image Scenes based on Object Shadow Lengths

Determining the Geographical Location of Image Scenes based on Object Shadow Lengths

Image-based geographical location estimation using web cameras

Geographical Location Estimation based on An Improved Particle Swarm Optimization

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