Papers
1
Total Citations
23
H-Index
1
About
KangUn Jo is a computer vision researcher whose work focuses on real-time object detection and multi-object tracking—critical technologies for applications in surveillance, gesture recognition, and robotic vision. His most influential contribution, the 2017 paper "A real-time multi-class multi-object tracker using YOLOv2," tackles the fundamental challenge of achieving high-speed, accurate tracking across multiple object classes simultaneously. By integrating YOLOv2’s fast detection framework with a tracking-by-detection approach, Jo demonstrated that real-time multi-class tracking is feasible without sacrificing performance, directly addressing the bottleneck of low processing speeds that had limited earlier systems. This work has accumulated 23 citations, reflecting its practical impact on researchers and engineers building efficient vision pipelines for dynamic environments. Jo’s research bridges the gap between theoretical tracking algorithms and real-world deployment constraints, making his contributions particularly valuable for autonomous systems and edge computing applications. His focus on balancing accuracy with computational efficiency continues to influence the development of lightweight, high-performance tracking solutions.
Research Focus
Key Achievements
Top Papers
- 1A real-time multi-class multi-object tracker using YOLOv223 citations · 2017