Heba Al-Hiary

About

Heba Al-Hiary is a computer vision researcher whose work focuses on intelligent transportation systems and pedestrian safety. Her most-cited paper, "Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier" (2019, 9 citations), introduces a robust detection framework that integrates multiple feature channels—including contour cues and census transform histograms—with a random forest classifier. This approach significantly improves detection accuracy in complex urban environments, addressing critical challenges in autonomous driving and surveillance. Al-Hiary’s contributions lie in fusing traditional handcrafted features with machine learning to enhance real-time object recognition. Her work has been cited by researchers advancing pedestrian detection, smart city infrastructure, and assistive technologies for the visually impaired. By combining contour-based cues with histogram features, she has helped refine how systems distinguish pedestrians from cluttered backgrounds. Al-Hiary’s research continues to influence the development of safer, more reliable perception systems in autonomous vehicles and robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier

   9 citations · 2019

Key Collaborators

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