Hamed Tabkhi

University of North Carolina at Charlotte

Papers

Total Citations

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About

Hamed Tabkhi is a leading researcher in embedded vision, real-time video analytics, and hardware-software co-design for autonomous systems. His work bridges the gap between advanced computer vision algorithms and resource-constrained embedded platforms, enabling intelligent perception in applications ranging from autonomous vehicles to social robotics and surveillance. Tabkhi’s major contributions include pioneering a convolutional approach for real-time pedestrian path prediction (CARPe Posterum, 2021), which has garnered 21 citations for its impact on safe navigation in dynamic environments. He also developed a hardware/software co-design framework for real-time object detection and tracking on embedded devices (2018), cited 7 times, demonstrating how to achieve high-performance vision processing within tight power and latency budgets. Additionally, his work on measuring compute-reuse opportunities for video processing acceleration (2019, 4 citations) provides foundational insights for efficient video analytics. Tabkhi’s research is notable for its practical focus on deployable systems, making him a key figure in advancing real-time, on-device intelligence for next-generation autonomous and monitoring technologies.