Guarav Shah

Virginia Commonwealth University, Bon Secours Health System

Papers

Total Citations

H-Index

About

Gaurav Shah is a robotics researcher whose work lies at the intersection of computer vision, sensor fusion, and autonomous navigation. His most cited paper, "Human tracking with an infrared camera using a curve matching framework" (2012, 16 citations), introduces the Curve Matched Kalman Filter (CMKF)—a novel algorithm that significantly improves human tracking accuracy for mobile robots by integrating curve matching with the traditional Kalman filter. This work addresses a critical challenge in dynamic environments where standard tracking methods often fail. Shah further advanced the field with his 2019 study on "Vegetation Segmentation for Sensor Fusion of Omnidirectional Far-Infrared and Visual Stream" (5 citations), which tackles the underexplored problem of vegetation perception for unmanned platforms. By fusing omnidirectional infrared and color vision data, his approach enhances a robot’s ability to understand and navigate complex outdoor terrains. Though his citation counts are modest, Shah’s contributions are technically significant, offering practical solutions for robust perception in real-world robotics. His work on CMKF, in particular, provides a foundational technique for researchers working on human-robot interaction and autonomous tracking systems.