Debajit Sarma
Papers
1
Total Citations
10
H-Index
1
About
Debajit Sarma is a researcher at the forefront of human-computer interaction, specializing in dynamic hand gesture recognition and computer vision. His major contribution lies in developing a novel two-stream fusion model that integrates 3D-CNN and 2D-CNN architectures, guided by optical flow motion templates, to significantly enhance the accuracy and robustness of gesture detection. This work, published in 2020 and garnering 10 citations, addresses critical challenges in real-world applications such as sign language interpretation and robotic surgery, where precise, real-time gesture understanding is essential. By fusing spatial and temporal features, Sarma’s approach offers a compelling solution for dynamic environments, bridging the gap between raw video data and meaningful interaction. His research not only advances the technical framework for gesture recognition but also underscores its practical impact in assistive technologies and medical robotics. For students and researchers exploring multimodal learning and human-robot interaction, Sarma’s work provides a foundational model for integrating deep learning with motion analysis, demonstrating how innovative fusion strategies can unlock new possibilities in non-verbal communication systems.
Research Focus
Key Achievements
Top Papers
- 1