A Grid-Based Index and Queries for Large-Scale Geo-tagged Video Collections

Ma, He; Ay, Sakire Arslan; Zimmermann, Roger; Kim, Seon Ho

doi:10.1007/978-3-642-29023-7_22

Cited by 2 publications

(1 citation statement)

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“…Authors [1,6] proposed a model to represent a camera viewable scene with a shape of a pie-slice, based on the GPS and digital compass data as well as the visible distance. Ma et al [7,8] proposed a grid-based index structure to speed up the searching process when dealing with directional or radius queries. They also use R-tree to index geo-referenced video with a two-level structure, i.e., the first level is a basic R-tree structure while the second level is a probability map attached to each leaf node of the R-tree.…”

Section: Related Work and Preliminariesmentioning

confidence: 99%

Double-Bounding R-tree: A Novel Approach to Index Digital Camera�s Viewable Scene

Ma¹,

Zhao²,

Wei³

et al. 2015

Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering

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Geographic Information System (GIS) applications now increasingly make use of geo-located multimedia data such as images and videos. Furthermore, the widespread use of smartphones (and increasingly tablets) and the rapid improvement of hardware has enabled the acquisition of high-definition user-generated videos that are annotated with geo-properties. The sensor meta-data (e.g., GPS and digital compass values), which associate a continuous stream of location and viewing direction information with the collected videos, are considerably smaller in size than the visual content and are helpful in effectively and efficiently manage and search through large repositories of videos. In this paper, we proposed the Double-Bounding R-tree (DBR-tree), which utilizes the inner-bounding rectangle and outer bounding rectangle to manage the camera's field of view. Experimental results show that this structure can prune the unnecessary overlap calculation and improve the spatial query performance.

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Section: Related Work and Preliminariesmentioning

confidence: 99%