Falconer: A Tethered Aerial Companion for Enhancing Personal Space

Nith, Romain; Rekimoto, Jun

doi:10.1109/vr.2019.8798367

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Unmanned aerial vehicles (UAVs) have gained popularity within the past few years as a means of acquiring data in a number of tasks [1,2]. In particular, tethered drones [3][4][5] have traded mobility and a short battery lifespan for the ability to sustain several hours of stationary flight, at heights up to 80 m above ground. The industry advertises them as temporary video surveillance cameras (https://elistair.com/, https://tethereddrones ystems.co.uk/, https://www.ntpdrone.com/), to which their video flux is indeed quite comparable.…”

Section: Introductionmentioning

confidence: 99%

Registering Unmanned Aerial Vehicle Videos in the Long Term

Lemaire

Crispim-Junior

Robinault³

et al. 2021

Sensors

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Unmanned aerial vehicles (UAVs) have become a very popular way of acquiring video within contexts such as remote data acquisition or surveillance. Unfortunately, their viewpoint is often unstable, which tends to impact the automatic processing of their video flux negatively. To counteract the effects of an inconsistent viewpoint, two video processing strategies are classically adopted, namely registration and stabilization, which tend to be affected by distinct issues, namely jitter and drifting. Following our prior work, we suggest that the motion estimators used in both situations can be modeled to take into account their inherent errors. By acknowledging that drifting and jittery errors are of a different nature, we propose a combination that is able to limit their influence and build a hybrid solution for jitter-free video registration. In this work, however, our modeling was restricted to 2D-rigid transforms, which are rather limited in the case of airborne videos. In the present paper, we extend and refine the theoretical ground of our previous work. This addition allows us to show how to practically adapt our previous work to perspective transforms, which our study shows to be much more accurate for this problem. A lightweight implementation enables us to automatically register stationary UAV videos in real time. Our evaluation includes traffic surveillance recordings of up to 2 h and shows the potential of the proposed approach when paired with background subtraction tasks.

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