Thomas B. Moeslund
Aalborg University
486 Papers
2.3K Citations
Thomas B. Moeslund is an academic researcher from Aalborg University. The author has contributed to research in topics: Computer science & Gesture. The author has an hindex of 43, co-authored 417 publications. Previous affiliations of Thomas B. Moeslund include MediaTech Institute.
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Papers
A survey of advances in vision-based human motion capture and analysis
TL;DR: This survey reviews recent trends in video-based human capture and analysis, as well as discussing open problems for future research to achieve automatic visual analysis of human movement.
3K
A Survey of Computer Vision-Based Human Motion Capture
Thomas B. Moeslund,Erik Granum +1 more
TL;DR: A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented, with a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition.
2.1K
Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey
TL;DR: A survey of the traffic sign detection literature, detailing detection systems for traffic sign recognition (TSR) for driver assistance and discussing future directions of TSR research, including the integration of context and localization.
Thermal cameras and applications: a survey
Rikke Gade,Thomas B. Moeslund +1 more
- 01 Jan 2014
TL;DR: An overview of the current applications of thermal cameras is provided, and the nature of thermal radiation and the technology of thermal camera are described.
Super-resolution: a comprehensive survey
Kamal Nasrollahi,Thomas B. Moeslund +1 more
- 01 Aug 2014
TL;DR: The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy, and common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super- resolution algorithms, and the most commonly employed databases are discussed.