Prateek Dayal
Papers
1
Total Citations
2
H-Index
1
About
Prateek Dayal’s research lies at the intersection of computer vision and edge computing, with a sharp focus on making AI accessible on resource-constrained devices. His most-cited work, “Efficient Person Detection on Single Board Computers: A Comparative Analysis of Algorithms” (2024, 2 citations), provides an exhaustive technical evaluation of person detection algorithms tailored for single-board computers (SBCs). By systematically comparing performance metrics like accuracy, speed, and memory usage, Dayal identifies optimal trade-offs for real-time deployment on platforms such as Raspberry Pi and Jetson Nano. This contribution is pivotal for advancing edge AI applications in surveillance, autonomous systems, and smart environments, where low-power, real-time detection is critical. Though early in his career, his work addresses a pressing need: bridging the gap between high-performance models and hardware limitations. Dayal’s analysis not only guides practitioners in selecting efficient algorithms but also highlights pathways for future optimization. His research underscores a commitment to democratizing AI, enabling robust person detection without reliance on cloud infrastructure—a key step toward scalable, privacy-preserving edge intelligence.
Research Focus
Key Achievements
Top Papers
- 1