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中文摘要：
SURF 算法广泛用于目标检测、跟踪和匹配等视频图像处理领域,但其计算复杂度高,在通用 CPU 上计算速度慢、实时性差,但 SURF 特征提取算法具备良好的可并行性。因此,根据现场可编程门阵列（FPGA）支持细粒度并行的特点,基于 HLS （High-level Synthesis）设计并实现了适合 FPGA 的 SURF 特征提取硬件加速单元。实验结果表明,相比通用 CPU,基于 FPGA 的 SURF 特征提取加速效果明显；相比 HDL 方式,基于 HLS 设计算法开发效率高、可移植性好。
英文摘要：
SURF （Speeded up robust features ） detection is used extensively in object detection,tracking and matching.However, it is computationally expensive and has poor real-time performance in general-purposed processors.Fortunately,SURF detection has high parallelism to be exploited.In this paper,hardware accelerator of SURF detection is implemented based on HLS to be executed on FPGAs.Experimental results show that SURF detection on FPGAs is much faster than that on CPUs.Furthermore,HLS is more productive and with better portability than traditional HDLs.
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