Rana Mostafa

Papers

Total Citations

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About

Rana Mostafa is a computer vision researcher whose work focuses on efficient multi-object tracking and detection for real-time applications. Her most-cited paper, "LMOT: Efficient Light-Weight Detection and Tracking in Crowds" (2022, 25 citations), addresses a critical bottleneck in robotics and autonomous systems: the trade-off between tracking accuracy and computational efficiency. Mostafa's key contribution lies in developing lightweight architectures that maintain robust tracking performance in crowded, dynamic environments while enabling deployment on resource-constrained platforms. By optimizing the balance between speed and precision, her work has practical implications for drones, surveillance systems, and autonomous vehicles that require real-time decision-making. Her research tackles the fundamental challenge of making computer vision pipelines both accurate and computationally feasible, a problem that has limited the adoption of multi-object tracking in real-world applications. Mostafa's approach demonstrates how careful architectural design can bridge the gap between state-of-the-art accuracy and practical deployment constraints, making her work valuable for researchers and engineers working on edge computing and embedded vision systems.