Chang-Beom Park

Papers

Total Citations

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About

Chang-Beom Park is a leading researcher in human-robot interaction (HRI), with a primary focus on real-time gesture recognition systems for mobile robotics. His most influential work, "Real-time 3D pointing gesture recognition for mobile robots with cascade HMM and particle filter" (2010), has garnered 71 citations, establishing him as a key contributor to natural HRI. Park’s major contribution lies in overcoming the persistent challenges of hand-tracking inaccuracies and unreliable gesture interpretation that plagued earlier systems. By integrating cascade Hidden Markov Models (HMM) with particle filters, he developed a robust framework capable of accurately recognizing 3D pointing gestures in dynamic, mobile environments—a critical advancement for intuitive robot control. His earlier foundational paper (2008, 23 citations) laid the groundwork for this approach, demonstrating real-time performance in mobile spaces. Park’s work has significantly improved the reliability of non-verbal human-robot communication, enabling more seamless and natural interactions. His research continues to influence the development of responsive robotic systems, making him a notable figure in the intersection of computer vision, machine learning, and robotics.