Measuring similarity between geo-tagged videos using largest common view

TL;DR

This paper introduces a new algorithm for measuring similarity between geo-tagged videos based on their field of view, effectively grouping trajectories by combining spatial locations and viewpoints, with improved efficiency demonstrated on real datasets.

Contribution

The paper proposes a novel algorithm that considers both spatial locations and views for trajectory similarity, along with methods to reduce computational costs.

Findings

Outperforms prior methods in accuracy

Reduces computational cost significantly

Effective on real-world datasets

Abstract

This paper presents a novel problem for discovering the similar trajectories based on the field of view (FoV) of the video data. The problem is important for many societal applications such as grouping moving objects, classifying geo-images, and identifying the interesting trajectory patterns. Prior work consider only either spatial locations or spatial relationship between two line-segments. However, these approaches show a limitation to find the similar moving objects with common views. In this paper, we propose new algorithm that can group both spatial locations and points of view to identify similar trajectories. We also propose novel methods that reduce the computational cost for the proposed work. Experimental results using real-world datasets demonstrates that the proposed approach outperforms prior work and reduces the computational cost.