RealROI: Discovering Real Regions of Interest From Geotagged Photos

Nam, Kwang Woo; Yang, KwangSoo

doi:10.1109/access.2022.3197169

Cited by 2 publications

(1 citation statement)

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“…A density-based algorithm contains two parameters (eps and minPts) to identify dense regions based on density reachability [31,32]. Density clustering algorithms are suitable for clusters of arbitrary shapes in geo-tagged photos and videos with FoVs [4,[33][34][35]. In order to better avoid the lossy problem, the FoVs clustering method in our paper is based on DBSCAN.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Discovering Homogeneous Groups from Geo-Tagged Videos

Lew

Nam

2023

Sensors

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The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets.

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