Imran Mahmud
Papers
1
Total Citations
12
H-Index
1
About
Imran Mahmud is a rising researcher at the intersection of the Internet of Things (IoT), computer vision, and embedded artificial intelligence. His work tackles a fundamental challenge in visual sensing: extracting three-dimensional spatial information from standard two-dimensional pixel matrices. In his highly cited paper, “Trackez: An IoT-Based 3D-Object Tracking From 2D Pixel Matrix Using Mez and FSL Algorithm,” Mahmud introduces a novel framework that leverages a Mez filter and Few-Shot Learning (FSL) to infer depth and track objects in real-time without specialized 3D sensors. This contribution is critical for resource-constrained IoT devices, enabling accurate spatial awareness from simple 2D cameras. With 12 citations since its 2023 publication, the work has already garnered attention for its practical approach to a persistent problem in robotics and surveillance. Mahmud’s research demonstrates a clear ability to bridge theoretical algorithms with deployable, low-cost hardware solutions. For students and researchers exploring edge computing, lightweight deep learning, or sensor fusion, Mahmud’s work offers a compelling model for how to push the boundaries of what is possible with minimal data.
Research Focus
Key Achievements
Top Papers
- 1