Cyril Briand
Papers
1
Total Citations
15
H-Index
1
About
Cyril Briand is a researcher whose work sits at the intersection of computer vision and mobile robotics, with a particular focus on real-time perception systems. His most-cited contribution, "Fast HOG based person detection devoted to a mobile robot with a spherical camera" (2013, 15 citations), addresses a critical challenge in autonomous navigation: enabling robots to detect humans efficiently using omnidirectional vision. In this work, Briand introduced a novel feature selection framework based on Binary Integer Programming, which optimizes the cascade of rejectors in Histogram of Oriented Gradients (HOG) detectors. This mathematical programming approach allows for the selection of highly discriminant features, significantly accelerating detection speed without sacrificing accuracy—a key requirement for resource-constrained mobile platforms. By tailoring person detection to spherical camera inputs, Briand's research contributes to safer and more responsive human-robot interaction in dynamic environments. His work demonstrates a thoughtful integration of optimization theory with practical robotic applications, offering a pathway toward more intelligent and perceptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1