Alexander Virgona

Papers

Total Citations

H-Index

About

Alexander Virgona’s research lies at the intersection of social robotics, computer vision, and human-robot interaction, with a focus on enabling robots to perceive and navigate human environments with greater social awareness. His most cited work, “Head-to-shoulder signature for person recognition” (2012, 22 citations), introduced a novel method for robots to identify and initiate interactions with specific individuals in groups, using scale-invariant head-to-shoulder patterns that work even as people move naturally. This foundational contribution addresses the complex challenge of unsolicited, targeted human-robot engagement in real-world settings. Virgona further advanced people tracking with “A robust people detection, tracking, and counting system” (2014, 11 citations), which improved unique person identification for accurate counting in dynamic spaces. His later work, “Socially Constrained Tracking in Crowded Environments Using Shoulder Pose Estimates” (2018), integrates shoulder pose cues to predict pedestrian motion, enabling robots to track individuals through dense crowds while respecting social norms. Collectively, his research has accumulated over 36 citations, demonstrating impact in developing perceptually aware robots that can operate seamlessly alongside humans in busy public spaces.