Ghalia Shariha

About

Ghalia Shariha’s research centers on computer vision and deep learning, with a particular focus on pedestrian detection and tracking for intelligent surveillance, autonomous vehicles, and robotics. Her most cited work, “Multiple Pedestrian Detection Depending on Faster Region-based Convolutional Neural Network (RCNN)” (2019), tackles the critical challenge of occlusion in multi-person tracking. By leveraging the high accuracy of Faster R-CNN, Shariha proposed a framework that improves detection reliability in crowded, real-world scenarios—a persistent hurdle in security and autonomous navigation systems. Though early in her citation impact, this contribution addresses a foundational problem in object detection, demonstrating her ability to apply advanced neural architectures to practical, safety-critical applications. Her work sits at the intersection of efficiency and robustness, aiming to make automated visual perception more dependable. As she continues to publish, Shariha’s research promises to strengthen the backbone of systems that rely on accurate, real-time pedestrian awareness.

Research Focus

Key Achievements

Top Papers

1. 1
   Multiple Pedestrian Detection Depending on Faster Region-based Convolutional Neural Network (RCNN)

   1 citations · 2019

Key Collaborators

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