Measuring similarity between geo-tagged videos using largest common view
This addresses the need for grouping moving objects and classifying geo-images in societal applications, but it appears incremental as it builds on prior spatial methods by adding view-based features.
The paper tackles the problem of measuring similarity between geo-tagged videos by considering both spatial locations and points of view, proposing a new algorithm that outperforms prior work and reduces computational cost.
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.