Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?

Aritra Dutta*, Srijan Das, Jacob Nielsen, Rajatshubhra Chakraborty, Mubarak Shah

*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar zenith angle, and population density of different geographies influence the data diversity. These factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground view data, including the open-world foundational models. To pave the way for a transformative era of aerial detection, we present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives - ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial view dataset, and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC, we recognize that augmenting object detectors with ground view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy, we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance aerial detection.
OriginalsprogEngelsk
Titel2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ForlagIEEE
Publikationsdatojun. 2024
Sider22678-22690
ISBN (Elektronisk)979-8-3503-5300-6
DOI
StatusUdgivet - jun. 2024
Begivenhed2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) - Seattle, USA
Varighed: 16. jun. 202422. jun. 2024

Konference

Konference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Land/OmrådeUSA
BySeattle
Periode16/06/202422/06/2024
NavnProceedings of the IEEE Conference on Computer Vision and Pattern Recognition
ISSN1063-6919

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