Deepika Deepika

Papers

Total Citations

H-Index

About

Deepika Deepika is a computer vision researcher whose work focuses on advancing intelligent transportation systems, particularly through automatic traffic sign recognition (TSR). Her most cited paper, "Histogram of Oriented Gradients Based Reduced Feature for Traffic Sign Recognition" (2018, 11 citations), addresses a critical challenge in autonomous driving and driver-assistance technologies. Deepika developed a novel approach using Histogram of Oriented Gradients (HOG) to create a reduced feature set that significantly improves the robustness and efficiency of TSR systems. This contribution is vital for real-time applications where accurate detection of traffic signs—such as speed limits, stop signs, and warnings—is essential for vehicle safety and navigation. Her work helps bridge the gap between computer vision algorithms and practical deployment in autonomous vehicles, assisting both human drivers and self-driving systems in interpreting road environments. By optimizing feature extraction, Deepika’s research enhances the reliability of TSR under varying lighting and weather conditions, directly impacting the development of safer, more intelligent transportation infrastructure. Her ongoing efforts continue to push the boundaries of vision-based automotive safety.