Utkarsh Shandilya
Papers
1
Total Citations
2
H-Index
1
About
Utkarsh Shandilya is a researcher at the forefront of integrating computer vision and deep learning for human-centric applications. His work primarily focuses on human action recognition, leveraging advanced pose estimation frameworks to enable machines to interpret and analyze human movement with high precision. In his most-cited paper, "Human Action Recognition Using Mediapipe Holistic Keypoints: A Deep Learning Approach" (2025), Shandilya introduces a novel methodology that combines Mediapipe’s holistic keypoint extraction with deep learning architectures to classify complex human actions. This contribution is particularly impactful for fields such as human-computer interaction, surveillance, and assistive technologies, offering a lightweight yet robust solution for real-time action recognition. With 2 citations already, his work is gaining traction among researchers seeking efficient, scalable approaches to pose-based action analysis. Shandilya’s research stands out for its practical applicability, bridging the gap between cutting-edge deep learning models and accessible, real-world deployment. His achievements underscore a commitment to advancing human-centered AI, making him a promising voice in the evolving landscape of computer vision and interactive systems.
Research Focus
Key Achievements
Top Papers
- 1