A High-Performance Dense Optical Flow Architecture Based on Red-Black SOR Solver

Abstract

Optical flow (OF) is an integral part of many vision systems, especially in the embedded and mobile application with ever-increasing challenges in achieving higher speed, minimal resource and lower power consumption. The work introduces a Dense High Throughput Optical Flow (DHTOF) architecture based on a novel fast converging Red-Black Successive Over Relaxation (RBSOR) solver architecture for computing dense and accurate OF using Horn and Schunck Optical Flow (HSOF) algorithm from Full High Definition (FHD) frames in real-time. The DHTOF architecture can capture dense OF from Ultra High Definition (UHD) frames at 48 Frames Per Second (FPS) with a throughput of 406 Megapixels/sec achieving a Throughput Per Watt (TPW) of 43 Giga Operation Per Second Per Watt (GOPS/Watt). The superscalar and deeply pipelined DHTOF architecture achieve same or lower Average Angular Error (AAE) with ≈ 4 × lesser number of RBSOR solver iterations as compared to the prior HSOF implementations based on Jacobi solver. It consumes 12.5 × lesser resources and 29.3% lower power for FHD resolution when compared to prior architectures. The proposed DHTOF architecture achieves highest area delay normalized speedup (at least by 28.2 ×) among the state of the art HSOF architectures. The successful evaluation of the proposed architecture for real-time OF sensor is demonstrated in Xilinx Virtex-VC707 Field Programmable Gate Array (FPGA) evaluation board.