Chandni
Papers
1
Total Citations
12
H-Index
1
About
Chandni is a researcher in computer vision, with a primary focus on human activity recognition (HAR) and deep learning. Her most-cited work, a 2017 survey on deep learning approaches for HAR, has garnered 12 citations and provides a comprehensive overview of how neural networks can identify human actions from video sequences—a critical capability for applications in smart surveillance, robot learning, human-computer interaction, and health assessment. This survey synthesizes key methodologies and challenges in the field, establishing her as a knowledgeable voice in HAR. Beyond this, her research explores the intersection of video analysis and automated understanding, contributing to the development of systems that can interpret complex human behaviors. Chandni’s work is particularly relevant for students and researchers seeking to understand the evolution of deep learning in activity recognition, offering a foundational resource that highlights both technical advancements and practical implications. Her contributions support the growing demand for intelligent, context-aware systems in security, healthcare, and robotics.
Research Focus
Key Achievements
Top Papers
- 1