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About

Tomasz Kryjak is a prominent researcher specializing in embedded vision systems, real-time image processing, and autonomous robotics, with a particular focus on FPGA-based hardware acceleration. His work bridges the gap between advanced computer vision algorithms and their practical deployment in resource-constrained, high-performance computing environments. Kryjak's most impactful contribution lies in optical flow implementation, where his 2022 paper on FPGA-accelerated Lucas-Kanade and Horn-Schunck algorithms for 4K video streams has garnered 29 citations, demonstrating significant community interest in efficient real-time motion estimation. He has further advanced the field through pioneering survey work on event-based vision systems on FPGAs, reflecting his commitment to emerging sensor technologies that excel under challenging lighting conditions. His research extends into multi-object tracking, combining state-of-the-art deep learning models like YOLOv8 with hardware acceleration on System-on-Chip FPGAs. Beyond vision systems, Kryjak explores autonomous aerial vehicles, investigating reinforcement learning strategies for drone navigation and control. This interdisciplinary breadth — spanning hardware design, computer vision, and autonomous systems — positions him as a versatile researcher whose contributions are increasingly shaping the future of real-time embedded intelligence in robotics and autonomous platforms.