Diana Baicu

Papers

Total Citations

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About

Diana Baicu is a researcher focused on computer vision and retail automation, with a particular interest in leveraging depth-sensing technologies to solve real-world inventory challenges. Her most cited work, "Determining on-shelf availability based on RGB and ToF depth cameras" (2021, 7 citations), introduces a novel method for calculating shelf occupancy using a vision pillar equipped with two RGB cameras and two Time-of-Flight (ToF) depth cameras. By scanning small shelf sections, this system determines the percentage of emptiness for products, directly addressing the critical retail problem of on-shelf availability. Baicu’s contribution lies in combining multimodal imaging—RGB for visual detail and ToF for precise depth mapping—to create a practical, non-intrusive solution for inventory monitoring. This work has implications for reducing out-of-stock scenarios and improving supply chain efficiency. While her citation count is still growing, her research demonstrates a clear application of computer vision to retail analytics, offering a scalable approach for automated shelf auditing. Baicu’s work stands out for its integration of affordable hardware with intelligent algorithms, making it accessible for real-world deployment in stores.