Papers
2
Total Citations
16
H-Index
2
About
Jin-Man Park is a researcher advancing the frontiers of computer vision and human-robot interaction. His primary research areas include change detection in visual surveillance, anomaly detection, and adaptive task planning for service robotics. Park’s most impactful contribution is his 2022 work on "Dual Task Learning," which tackles the critical challenge of robust change detection under imperfect image matching—a common real-world problem in surveillance and mobile robotics. By leveraging both dense correspondence and mis-correspondence, his approach significantly improves accuracy in dynamic environments, earning 13 citations and establishing a new benchmark for the field. Additionally, Park’s 2018 paper on an "Adaptive Task Planner" demonstrates his expertise in enabling robots to perform home service tasks through cooperative human-robot interaction. This work introduces a memory-and-reasoning-based planner that allows robots to adapt to environmental changes in real time, a key step toward practical domestic robotics. With a focus on bridging theoretical algorithms and real-world applications, Park’s research holds promise for advancing autonomous systems in safety-critical and everyday settings.
Research Focus
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Top Papers
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