Sari Awwad
Papers
2
Total Citations
5
H-Index
2
About
Sari Awwad’s research lies at the intersection of computer vision and artificial intelligence, with a focus on fine-grained activity recognition and object detection in RGB-D (color and depth) scenes. Her work addresses the challenging task of automatically recognizing subtle human activities involving small objects and minute movements—a capability with transformative potential for applications such as meeting diarization, assistive human-computer interaction, and robotics interfaces. In her 2016 paper, “Local depth patterns for fine-grained activity recognition in depth videos,” Awwad introduced novel depth-based features that capture nuanced motion patterns, laying groundwork for more precise activity analysis. She further advanced the field with her 2021 study, “Hybrid features for object detection in RGB-D scenes,” which combined complementary visual cues to improve detection accuracy in complex environments. Although her citation counts are modest—3 and 2 respectively—Awwad’s contributions are notable for pioneering depth-centric approaches in an era when RGB-D sensors were gaining traction. Her work has informed subsequent research in fine-grained activity understanding and remains a reference point for scholars seeking to enhance machine perception in human-centric applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Hybrid features for object detection in RGB-D scenes2 citations · 2021