R. R. Dharun Raagav
Papers
1
Total Citations
4
H-Index
1
About
R. R. Dharun Raagav is a researcher at the forefront of computer vision and video processing, with a particular focus on optimizing deep learning models for modern video compression standards. His most cited work introduces a novel and optimized YOLO model specifically designed for object detection in H.265 encoded video frames, addressing the critical challenge of maintaining detection accuracy while processing highly compressed video streams. This contribution is especially relevant for real-time surveillance and autonomous systems, where efficient video coding is essential. Although early in his career, his work has already garnered attention, with his flagship paper accumulating 4 citations. Raagav’s research bridges the gap between advanced video codecs and state-of-the-art object detection, demonstrating a keen ability to solve practical engineering problems. His achievements mark him as an emerging voice in the intersection of video compression and computer vision, with potential for significant impact as his methods are adopted in bandwidth-constrained applications.
Research Focus
Key Achievements
Top Papers
- 1A novel and optimized YOLO model for H.265 encoded video frames4 citations · 2025