Soumi Ghosh
Papers
1
Total Citations
3
H-Index
1
About
Soumi Ghosh is a researcher whose work sits at the intersection of computer vision, object tracking, and intelligent surveillance systems. Her primary research focuses on developing mathematical approaches to enhance vigilance and monitoring technologies, with particular emphasis on visual tracking techniques for real-world applications. Her most-cited paper, "Vigilance and surveillance reinforced using mathematical approaches in object tracking techniques" (2024), addresses critical challenges in automated people counting and object recognition across diverse domains including robotics, traffic monitoring, autonomous vehicles, and forensic analysis. This work has already garnered 3 citations, demonstrating early impact in the field. Ghosh's contributions are particularly relevant to advancing the reliability and accuracy of surveillance systems, where mathematical modeling improves tracking performance in complex environments. Her research bridges theoretical mathematical frameworks with practical computer vision applications, offering solutions that enhance security infrastructure and autonomous system functionality. As a developing scholar, Ghosh's work represents an important step toward more robust and intelligent visual monitoring systems that can operate effectively in dynamic, real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 1