Gabriel Kerekes

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About

Gabriel Kerekes is a researcher whose work bridges the critical intersection of computer vision, robotics, and geospatial engineering. His primary research focuses on developing robust, image-based methods for target detection and tracking, particularly through the innovative use of image-assisted robotic total stations. Kerekes’s most significant contribution to date is his 2019 paper, "Image-Based Target Detection and Tracking Using Image-Assisted Robotic Total Stations," which has garnered 9 citations and serves as a foundational reference for integrating visual data with precise surveying instruments. This work addresses the challenge of automating the detection and continuous tracking of targets in dynamic environments, offering practical solutions for applications in construction, infrastructure monitoring, and autonomous navigation. By combining real-time image processing with robotic total station technology, Kerekes has advanced the accuracy and efficiency of spatial data collection, enabling more reliable automated workflows. His research is particularly valuable for students and practitioners in geomatics and robotics, as it demonstrates how traditional surveying tools can be enhanced with modern computer vision techniques to solve real-world problems. Kerekes’s work continues to influence the development of smarter, more autonomous measurement systems.