Archita Tah
Papers
1
Total Citations
9
H-Index
1
About
Archita Tah is a computer vision researcher whose work focuses on advancing object detection, tracking, and segmentation—critical technologies for video surveillance, autonomous navigation, and robotics. Her most-cited paper, "Moving Object Detection and Segmentation using Background Subtraction by Kalman Filter" (2017, 9 citations), tackles the fundamental challenge of accurately isolating moving objects in dynamic scenes. In this work, Tah introduces a robust approach that combines background subtraction with Kalman filtering to predict and refine object trajectories, significantly reducing noise and false detections. This contribution is especially valuable for real-time applications where precision and computational efficiency are paramount. Beyond this flagship study, Tah’s research explores the intersection of signal processing and machine learning to enhance motion analysis in cluttered environments. Her work has been cited by peers developing autonomous systems and intelligent surveillance frameworks, underscoring its practical impact. By addressing core bottlenecks in visual tracking, Archita Tah continues to shape how machines perceive and interact with moving objects, making her a notable voice in the evolving field of computer vision.
Research Focus
Key Achievements
Top Papers
- 1