Gun-Gyo In
Papers
4
Total Citations
49
H-Index
4
About
Gun-Gyo In is a researcher at the forefront of deploying deep learning for real-world robotic and edge-AI systems. His work centers on computer vision, particularly object detection, pedestrian tracking, and 3D recognition, with a strong emphasis on optimizing models for resource-constrained edge devices. In’s most impactful contribution is his 2022 paper on the GuardBot edge deployment framework for face mask recognition, which has garnered 24 citations and addresses the critical challenge of achieving real-time inference on edge hardware. He has also advanced autonomous robotics through a comprehensive survey on 3D recognition sensor modalities (12 citations) and developed the SPT framework, a Siamese model-based single pedestrian tracking system (8 citations) vital for surveillance and human-following robots. His practical evaluations of YOLOv3 and YOLOv4 detectors for elevator button recognition on mobile robots further demonstrate his commitment to bridging the gap between AI research and deployable solutions. With a growing citation record, In is establishing himself as a key contributor to efficient, production-ready vision systems for autonomous platforms.
Research Focus
Key Achievements
Top Papers
- 1
- 23D Recognition Based on Sensor Modalities for Robotic Systems: A Survey12 citations · 2021
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