Discovering Homogeneous Groups from Geo-Tagged Videos

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.

Automated summary

In this paper, algorithms are proposed to discover homogeneous groups from geo-tagged videos with view directions. The density clustering algorithm is also extended to support fields-of-view (FoVs) in the geo-tagged videos, and an optimization model based on a two-level grid-based index is proposed. Experimental evaluation on real and synthetic datasets demonstrates the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach.