Abhijeet Shenoi
Papers
4
Total Citations
329
H-Index
4
About
Abhijeet Shenoi is a leading researcher at the intersection of computer vision and robotics, specializing in human-robot interaction and autonomous navigation. His work focuses on enabling robots to perceive, reason about, and predict human behavior in complex, built environments. Shenoi’s most impactful contribution is the development of **spatiotemporal relationship reasoning** for pedestrian intent prediction, a framework that allows robots to forecast future actions by analyzing the dynamic relationships between people and their surroundings. This work, published in 2020, has garnered **185 citations**, underscoring its influence on the field. He is also the co-creator of the **JRDB dataset** (117 citations), a large-scale, egocentric benchmark that has become a standard resource for training and evaluating models on human perception from a robot’s perspective. Additionally, his work on **JRMOT**, a real-time 3D multi-object tracker, advances the ability of robots to track multiple agents in 3D space—a critical capability for safe navigation. Through these contributions, Shenoi has provided both foundational datasets and novel reasoning frameworks that push the boundaries of how robots understand and interact with the dynamic world around them.
Research Focus
Key Achievements
Top Papers
- 1Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction185 citations · 2020
- 2
- 3JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset17 citations · 2020
- 4Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction10 citations · 2020