Vartika Sengar
Papers
1
Total Citations
5
H-Index
1
About
Vartika Sengar is a researcher at the intersection of computer vision, robotics, and retail automation, with a focus on developing intelligent systems for real-world operational challenges. Her most cited work, "Concept-Based Anomaly Detection in Retail Stores for Automatic Correction Using Mobile Robots" (2023, 5 citations), introduces a novel approach that moves beyond traditional planogram compliance for inventory management. Instead of relying solely on predefined shelf layouts, Sengar’s method leverages concept-based anomaly detection, enabling mobile robots to identify and correct misplaced items autonomously. This work addresses one of the most labor-intensive tasks in retail—tracking and rearranging inventory—by combining vision-based techniques with robotic intervention. Her contribution is significant for its potential to reduce human workload and improve store efficiency through automation. While her citation count is still growing, Sengar’s research stands out for its practical applicability and innovative integration of conceptual reasoning into robotic systems. Her work is particularly relevant for students and researchers interested in applied AI, autonomous navigation, and the future of smart retail environments, where robots are not just observers but active participants in maintaining order.
Research Focus
Key Achievements
Top Papers
- 1