Shah Rutvik Vrajesh
Papers
2
Total Citations
23
H-Index
2
About
Shah Rutvik Vrajesh is a researcher focused on advancing computer vision and deep learning for real-time, resource-constrained applications. His primary research areas include lightweight object detection, small-object recognition, and trajectory prediction. His most notable contribution, "RFSOD: a lightweight single-stage detector for real-time embedded applications to detect small-size objects" (2021, 17 citations), addresses the critical challenge of deploying accurate detection models on embedded systems with limited computational power. This work has significant implications for robotics, surveillance, and autonomous systems. Additionally, his paper "Shuttlecock Detection and Fall Point Prediction using Neural Networks" (2020, 6 citations) demonstrates his interest in dynamic object tracking and trajectory forecasting, applying neural networks to predict the landing point of a moving shuttlecock in sports analytics. Through these contributions, Shah has shown a commitment to bridging the gap between theoretical deep learning and practical, real-world deployment, making his work valuable for engineers and researchers developing efficient vision systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Shuttlecock Detection and Fall Point Prediction using Neural Networks6 citations · 2020