Sana Liaquat
Papers
1
Total Citations
8
H-Index
1
About
Sana Liaquat is a researcher whose work sits at the intersection of computer vision and robotics, with a particular focus on enabling machines to perceive and interact with three-dimensional environments using minimal hardware. Her most cited paper, "Object detection and depth estimation of real world objects using single camera" (2015, 8 citations), tackles a fundamental challenge in the field: how to extract depth information from a single, monocular camera feed. Rather than relying on expensive stereo rigs or specialized sensors, Liaquat’s proposed technique intelligently processes images of cluttered indoor environments—such as a room’s walls—to detect objects of interest and then estimate their distance by calculating their apparent area against a training set. This work is notable for its practical, low-cost approach to a problem that typically demands complex equipment, making depth estimation more accessible for applications like assistive robotics, autonomous navigation, and augmented reality. By demonstrating that meaningful spatial data can be extracted from a single, everyday camera, Liaquat has contributed a valuable stepping stone toward more efficient and deployable vision systems, showing that sometimes the most elegant solutions come from doing more with less.
Research Focus
Key Achievements
Top Papers
- 1