Guillaume Delorme
Papers
1
Total Citations
4
H-Index
1
About
Guillaume Delorme is a researcher at the intersection of robotics, computer vision, and audio processing, with a focus on multi-person tracking and human-robot interaction. His most cited work, "Audio-Visual Variational Fusion for Multi-Person Tracking with Robots" (2019), introduces a novel variational framework that integrates auditory and visual cues to robustly track multiple individuals in dynamic, real-world environments—a critical capability for autonomous robots operating in crowded spaces. Though early in its citation impact (4 citations), this paper has been presented to an international audience, signaling its relevance to the growing field of multi-modal perception. Delorme’s contributions lie in advancing sensor fusion techniques that leverage the complementary strengths of sound and sight, enabling robots to maintain situational awareness even when visual data is occluded or noisy. His work is particularly valuable for applications in service robotics, surveillance, and collaborative human-robot teams. By tackling the challenge of multi-person tracking from a moving platform, Delorme helps bridge the gap between laboratory algorithms and practical, real-time deployment.
Research Focus
Key Achievements
Top Papers
- 1Audio-Visual Variational Fusion for Multi-Person Tracking with Robots4 citations · 2019