Besma Abidi

University of Tennessee at Knoxville, Intelligent Systems Research (United States)

Papers

Total Citations

188

H-Index

About

Besma Abidi’s research lies at the intersection of computer vision, sensor planning, and autonomous surveillance, where she has pioneered methods for real-time object tracking and multimodal sensor integration. Her most influential work, an optical flow-based real-time tracking system using a non-prior training active feature model (88 citations), introduced a robust framework for dynamic object following without requiring pre-learned templates—a critical advance for unconstrained environments. In her comprehensive survey on multimodal sensor planning for wide-area surveillance (67 citations), Abidi systematically analyzed decades of research, revealing how most prior work was confined to laboratory settings for 3D reconstruction and robotic manipulation, and charting a path toward scalable, real-world deployment. Her applied contributions include a laser ranging and video imaging system for industrial bin picking (27 citations), which fused 2D imagery with precise 3D models to enable reliable robotic grasping of unordered parts. Abidi’s work on cooperative fixed and PTZ camera networks further demonstrated how static and active sensors could collaborate for automatic target acquisition and tracking. With over 180 total citations, her research has advanced both the theoretical foundations and practical implementations of intelligent vision systems, particularly in security, manufacturing, and autonomous navigation.