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Manuel Muehlhaus

dblp:359/9905 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0007-2298-9058ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Video understanding and tracking · 87% 3D vision · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking
multi-object tracking
0.812024
VETRA: A Dataset for Vehicle Tracking in Aerial Imagery - New Challenges for Multi-Object Tracking · ECCV (85) 2024
Computer vision › Video understanding and tracking › object tracking
UAV tracking
0.812024
VETRA: A Dataset for Vehicle Tracking in Aerial Imagery - New Challenges for Multi-Object Tracking · ECCV (85) 2024
Computer vision › 3D vision › remote sensing
aerial imagery
0.212024
VETRA: A Dataset for Vehicle Tracking in Aerial Imagery - New Challenges for Multi-Object Tracking · ECCV (85) 2024

Methods — techniques the papers use, named apart from their topics

multi-object tracking · 0.8
YearPublicationVenuePosition
2024 VETRA: A Dataset for Vehicle Tracking in Aerial Imagery - New Challenges for Multi-Object Tracking
Jens Hellekes, Manuel Muehlhaus, Reza Bahmanyar, Seyed Majid Azimi, Franz Kurz
ECCV (85)2
2023 MACS-HAP: Design and Image Processing Features of the DLR HAP Camera System
abstract
Recent developments in the construction and operation of high-altitude platforms (HAPs) close the gap between airborne and spaceborne remote sensing. The possibility of permanent monitoring of regions from the stratosphere offers new fields of application requiring bespoke instruments for operation on HAP carriers.The MACS-HAP aerial camera system was developed specifically for platforms operating in the stratosphere. Based on a refractive optical design, it captures aerial images with 127 megapixels per image and achieves a ground sampling distance of 15 cm per pixel at 20 km altitude. One of the special system features is the embedded onboard processing hardware with specific algorithms for real-time information extraction.This paper presents an overview of the instrument and the current state of development. Technical solutions to meet the specific payload requirements are described, such as stress-optimized design and the lightweight optical system with its capabilities for automatic onboard image processing.
Jörg Brauchle, Veronika Gstaiger, Seyed Majid Azimi, Manuel Muehlhaus, Florian Nikodem, Patrick Dern, Derek Griffith
IGARSS4