Using C to implement high-efficient computation of dense optical flow on FPGA-accelerated heterogeneous platforms

Abstract

High-quality algorithms for dense optical flow computation are computationally intensive. To compute them with high speed and low power is vital to make optical flow computation applicable in real-world applications. In contrast to only the Horn-Schunck model being studied on FPGA-based systems today, one of the best linear variational methods for dense optical flow computation, Combine-Brightness-Gradient, is implemented on FPGA-accelerated heterogeneous platforms in this paper. C instead of HDLs is employed and optimizing techniques based on the algorithmic parallelism and hardware architecture are introduced. Experimental results show that 30–110x improvement of the computing efficiency over CPUs was achieved. The FPGA-accelerated version is able to process 640 × 480 image at 12 fps with 0.38 J per frame, while it is 0.8 fps and around 40 J on CPUs. Through demonstrating high performance and low power of dense optical flow algorithm on FPGA-based heterogeneous platforms implemented in C, this paper shows that the off-the-shelf commodity FPGAs coupled with High-Level-Synthesis (HLS) tools could provide an available option when computational efficiency together with development speed are required.