Shilpa Mehta
Papers
2
Total Citations
5
H-Index
2
About
Dr. Shilpa Mehta is a researcher at the intersection of computer vision and autonomous robotics, with a particular focus on enabling robots to perceive and interact with dynamic environments. Her work centers on object detection for robotic platforms, especially within the challenging RoboSoccer domain—a standard testbed for evaluating real-time vision and decision-making algorithms. In her 2022 study on HOG-based object detection for soccer-playing robots, she demonstrated how traditional feature descriptors can be effectively applied to fast-paced robotic scenarios, earning 3 citations. She further advanced this line of inquiry by exploring convolutional neural networks for object detection in the same environment, showing how deep learning can enhance autonomous perception. Although her citation counts are modest, her contributions are foundational to the practical deployment of vision systems in competitive robotics. Dr. Mehta’s research is particularly valuable for students and engineers working on embedded vision, as it bridges classical computer vision techniques with modern deep learning approaches, offering reproducible insights for real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1HOG-Based Object Detection Toward Soccer Playing Robots3 citations · 2022
- 2