Zezhi Chen

Papers

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About

Zezhi Chen is a researcher specializing in mobile robot navigation and computer vision, with a focus on enabling autonomous vehicles to perceive and interact with their environments. His key contributions lie in visual navigation, obstacle detection, and real-time tracking, where he has developed innovative algorithms for ground plane segmentation and object height measurement. In his highly cited work "Mobile Robot Visual Navigation Using Multiple Features" (14 citations), Chen proposed a method to segment the ground plane from a robot’s field of view, allowing it to distinguish between traversable terrain and obstacles. This work directly addresses critical challenges in autonomous navigation. He further advanced real-time tracking in "Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform" (13 citations), where he exploited variable kernels to improve tracking performance in video sequences. Another notable contribution is "Monocular obstacle detection using reciprocal-polar rectification" (8 citations), which enhances obstacle detection from a single camera. Chen’s research has practical implications for semi-autonomous vehicle design, and his algorithms have been tested on real-world testbed platforms, demonstrating their utility in dynamic environments. His work continues to influence the fields of robotics and autonomous systems.