Papers
3
Total Citations
33
H-Index
3
About
Ariane Herbulot is a researcher whose work lies at the intersection of computer vision, robotics, and embedded systems, with a primary focus on real-time person detection and tracking. Her major contributions center on developing efficient, computationally-light algorithms that enable mobile robots to perceive and follow humans in dynamic environments. Her most cited work, "Fast HOG based person detection devoted to a mobile robot with a spherical camera" (2013, 15 citations), introduces a novel feature selection framework using Binary Integer Programming to accelerate Histogram of Oriented Gradients (HOG) detection within a cascade-of-rejectors structure—a critical advancement for resource-constrained robotic platforms. She further advanced cooperative perception in "Cooperative passers-by tracking with a mobile robot and external cameras" (2012, 14 citations), demonstrating how robots can leverage external camera networks to maintain robust tracking of moving individuals. Her earlier work, "Active Method for Mobile Object Detection from an Embedded Camera, Based on a Contrario Clustering" (2011, 4 citations), pioneered a contrario clustering for active object detection, reducing false positives in embedded camera feeds. Herbulot’s contributions are particularly notable for their practical impact on autonomous navigation and human-robot interaction, offering scalable solutions that balance accuracy with the strict computational budgets of mobile platforms.
Research Focus
Key Achievements
Top Papers
- 1
- 2Cooperative passers-by tracking with a mobile robot and external cameras14 citations · 2012
- 3