Prasenjit Das
Papers
1
Total Citations
9
H-Index
1
About
Dr. Prasenjit Das is a computer vision researcher whose work focuses on advancing object detection, tracking, and segmentation for real-world applications like video surveillance and autonomous navigation. His most cited paper, "Moving Object Detection and Segmentation using Background Subtraction by Kalman Filter" (2017, 9 citations), introduces a robust framework that combines background subtraction with Kalman filtering to improve the accuracy and stability of moving object tracking in dynamic environments. This contribution addresses a critical challenge in computer vision—handling noise and occlusion—by leveraging predictive filtering to maintain object continuity. Dr. Das’s research has practical implications for robotics, intelligent transportation, and security systems, where reliable object tracking is essential. His work demonstrates a strong commitment to developing efficient, real-time solutions that bridge the gap between theoretical algorithms and deployable systems. With a focus on motion analysis and scene understanding, Dr. Das continues to contribute to the growing field of visual surveillance and autonomous perception, making his research valuable for students and engineers working on next-generation vision-based technologies.
Research Focus
Key Achievements
Top Papers
- 1