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Registering Unmanned Aerial Vehicle Videos in the Long Term

Université Lumière - Lyon 2, LIRIS, UMR5205, F-69676 Lyon, France
Foxstream, F-69120 Vaulx-en-Velin, France
Authors to whom correspondence should be addressed.
Sensors 2021, 21(2), 513;
Received: 19 November 2020 / Revised: 11 December 2020 / Accepted: 6 January 2021 / Published: 13 January 2021
(This article belongs to the Section Remote Sensors)
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. View Full-Text
Keywords: registration; stabilization; unmanned aerial vehicle; drone registration; stabilization; unmanned aerial vehicle; drone
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MDPI and ACS Style

Lemaire, P.; Crispim-Junior, C.F.; Robinault, L.; Tougne, L. Registering Unmanned Aerial Vehicle Videos in the Long Term. Sensors 2021, 21, 513.

AMA Style

Lemaire P, Crispim-Junior CF, Robinault L, Tougne L. Registering Unmanned Aerial Vehicle Videos in the Long Term. Sensors. 2021; 21(2):513.

Chicago/Turabian Style

Lemaire, Pierre, Carlos F. Crispim-Junior, Lionel Robinault, and Laure Tougne. 2021. "Registering Unmanned Aerial Vehicle Videos in the Long Term" Sensors 21, no. 2: 513.

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