Papers
1
Total Citations
10
H-Index
1
About
Dasol Hong 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 outdoor surveillance in real environments. This work, published in 2023 and already garnering 10 citations, addresses critical gaps in deep learning-based surveillance tasks such as human detection, tracking, and motion recognition. By integrating diverse sensor modalities, Hong’s dataset enables more robust and realistic evaluation of algorithms, pushing the boundaries of autonomous systems operating in complex, uncontrolled settings. Their research bridges the gap between theoretical deep learning models and practical deployment, offering a foundational resource for the community. Hong’s work is particularly impactful for students and researchers seeking to benchmark their own models against real-world challenges, making them a key figure in the evolution of intelligent surveillance and robotic perception.
Research Focus
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Top Papers
- 1