JungHyuk Im

Daejeon Institute of Science and Technology

Papers

Total Citations

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About

JungHyuk Im is a computer vision researcher whose work focuses on real-time object detection and multi-object tracking for practical, real-world applications. His most impactful contribution, the paper "A real-time multi-class multi-object tracker using YOLOv2" (2017), has garnered 23 citations and addresses a critical challenge in surveillance, gesture recognition, and robotic vision: achieving high-speed, accurate tracking across multiple object classes simultaneously. By integrating the YOLOv2 detection framework with efficient tracking algorithms, Im demonstrated how to overcome the processing bottlenecks that had previously limited real-time performance in complex, dynamic environments. His research bridges the gap between theoretical advances in deep learning and the stringent speed requirements of embedded and autonomous systems. Im’s work is particularly notable for its emphasis on practical deployability, offering a scalable solution for applications ranging from security monitoring to human-robot interaction. Through this key publication, he has helped advance the state of the art in multi-class tracking, providing a foundation for subsequent developments in real-time computer vision systems.