VVS: Fast Similarity Measuring of FoV-Tagged Videos

In this article, we propose a novel method that can measure the similarity of FoV-tagged videos in two dimensions. Recently many researchers have focused on measuring the similarity of FoV-tagged videos. The similarity measurement of FoV-tagged videos plays an important role in various societal applications, including urban road networks, traffic, and geographic information systems. Our preliminary work introduced the Largest Common View Subsequences (LCVS) algorithm for computing the similarity of FoV-tagged videos. However, LCVS requires a high computational cost for calculating common viewable regions between two FoV-tagged videos. To handle this limitation, we propose the largest View Vector Subsequence (VVS) algorithm for reducing the computational cost of FoV-tagged videos. VVS uses the movement distances and the viewable direction distances to support the simplified vector-based similarity measurement. We demonstrate the superiority of our approach by comparing it with the Longest Common Subsequences (LCSS) and our prior work (LCVS). Our experimental results show that VVS outperforms the prior work in terms of the computational cost and enhances the versatility and stability of the similarity measurement.