Daeho Um
Papers
1
Total Citations
10
H-Index
1
About
Daeho Um is a researcher at the forefront of multi-modal sensor systems and outdoor surveillance, with a focus on advancing robotics and computer vision. Their most notable contribution is the development of the X-MAS dataset—an extremely large-scale multi-modal sensor dataset designed for real-world outdoor surveillance. This work, published in 2023 and already garnering 10 citations, addresses critical gaps in existing benchmarks by integrating diverse sensor modalities, enabling more robust human detection, tracking, and motion recognition in challenging environments. By emphasizing deep learning algorithms for these tasks, Um’s research bridges the gap between theoretical computer vision and practical deployment in security and autonomous systems. The X-MAS dataset stands out for its scale and realism, providing a vital resource for training and evaluating models under uncontrolled conditions. Um’s work is particularly impactful for students and researchers seeking to push the boundaries of surveillance technology, offering a foundation for safer, more intelligent outdoor monitoring systems.
Research Focus
Key Achievements
Top Papers
- 1