Bodam Nam

Papers

Total Citations

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About

Bodam Nam is a robotics researcher whose work focuses on real-time perception systems for mobile robots, with particular emphasis on pedestrian detection and GPU-accelerated computer vision. His major contributions lie in developing efficient vision-based detection methods that enable mobile robots to navigate safely in human environments. Nam's most cited work, "People detection method using graphics processing units for a mobile robot with an omnidirectional camera" (2011, 10 citations), introduces a novel approach that leverages GPU computing to compute dense optical flow maps, allowing robots to identify regions of interest by analyzing compliance with ego-motion. His complementary study, "Pedestrian detection system based on stereo vision for mobile robot" (2011, 9 citations), advances this work by integrating stereo vision with GPU-accelerated disparity mapping for real-time pedestrian detection. Together, these papers demonstrate Nam's pioneering application of parallel computing to overcome the computational bottlenecks of real-time robotic vision. By combining omnidirectional cameras, stereo vision, and GPU processing, Nam has helped lay the groundwork for more responsive and safer mobile robots capable of operating alongside humans in dynamic environments.