Kishore Konda

Fraunhofer Institute for Communication, Information Processing and Ergonomics, Goethe University Frankfurt

Papers

Total Citations

H-Index

About

Kishore Konda’s research lies at the intersection of human-robot interaction and computer vision, with a focus on real-time gesture and activity recognition. His most cited work, “Real time interaction with mobile robots using hand gestures” (2012, 28 citations), introduced a robust system that enables intuitive, on-site control of outdoor mobile robots through hand gesture commands—a foundational contribution to natural human-robot communication. Konda further advanced the field with “Real-time activity recognition via deep learning of motion features” (2015, 3 citations), where he pioneered a deep learning approach to extract energy-based motion features directly from video blocks, enabling efficient, real-time activity classification. This work demonstrates his early adoption of deep learning for motion analysis, a technique that has since become central to modern computer vision. While his citation counts reflect a focused, emerging career, Konda’s contributions are notable for their practical, real-time implementation—bridging the gap between algorithmic innovation and deployable robotic systems. His research offers valuable insights for students and researchers exploring gesture-based interfaces, mobile robotics, and deep learning for video understanding.