Papers
3
Total Citations
89
H-Index
2
About
Vijeta Sharma is a leading researcher at the intersection of computer vision and edge artificial intelligence, with a primary focus on human activity recognition (HAR) and pose estimation. Her most impactful work, a comprehensive 2022 review of deep learning-based HAR on benchmark video datasets—garnering 85 citations—established a critical taxonomy of AI techniques for behavior analysis, scene understanding, and event recognition from video data. Sharma’s contributions extend beyond surveys; she has pioneered practical implementations using MediaPipe holistic keypoints for real-time human action recognition and advanced the field of Edge AI by demonstrating how high-performance TPU computing can enhance human pose estimation in robotic systems. Her recent 2025 papers, while early in their citation lifecycle, signal a strategic shift toward deploying sophisticated deep learning models on resource-constrained edge devices, bridging the gap between cloud-based AI and real-world robotics. By combining rigorous methodological reviews with hands-on engineering for efficient, on-device inference, Sharma is shaping how machines interpret human motion—from surveillance to assistive robotics—making her work essential for students and researchers seeking to understand both the theoretical foundations and practical deployment of vision-based AI systems.
Research Focus
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Top Papers
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