Sheng Lin-cheng
Papers
1
Total Citations
2
H-Index
1
About
Sheng Lin-cheng is a researcher whose work bridges robotics, computer vision, and autonomous systems, with a particular focus on real-time environmental perception for mobile robots. His most cited paper, "Real-Time Detection and Tracking of Traffic Sign in Video Sequences for Autonomous Mobile Robot" (2012), introduces a practical framework for enabling autonomous vehicles to detect and track traffic signs in dynamic video streams. By leveraging the Continuous Adaptive Mean Shift (Cam-Shift) algorithm, Lin-cheng demonstrates how high-speed, illumination-insensitive tracking can be achieved—a critical capability for safe navigation in real-world settings. Though his citation count is modest, his contributions are notable for their applied, systems-level approach, directly addressing the computational and robustness challenges of deploying vision-based autonomy on platforms like the P3-AT mobile robot. His work underscores the importance of efficient, real-time algorithms in the early development of autonomous navigation, offering foundational insights for students and researchers exploring the intersection of computer vision and mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1