Tamal Datta
Papers
2
Total Citations
8
H-Index
2
About
Tamal Datta is a researcher in autonomous systems and computer vision, with a focused interest in real-time perception for ground vehicles. His work centers on developing robust algorithms for detection, tracking, and lane-line identification, which are critical for enabling self-driving cars to navigate safely. Datta’s most cited paper, “Real-Time Tracking and Lane Line Detection Technique for an Autonomous Ground Vehicle System” (2019), has garnered 6 citations, demonstrating its relevance in the field. In a complementary study, he introduced a procedure for real-time detection and tracking of a moving model car using a Kalman filter, where raw image acquisition and background subtraction are employed to isolate the vehicle before applying the filter for smooth, predictive tracking. This work, cited 2 times, offers a practical, low-cost framework for dynamic object tracking. Datta’s contributions are valuable for students and researchers exploring sensor fusion and state estimation in autonomous navigation, providing clear, implementable methods that bridge theory and application. His research continues to inform advancements in real-time, vision-based control systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Real Time Detection and Tracking of a Model Car using Kalman Filter2 citations · 2019