Scholarlink

<p>Dense and accurate optical flow estimation is an important requirement for dynamic scene perception in autonomous systems. However, most of the existing FPGA accelerators are based on classic methods, which cannot deal with large displacements of moving objects in ultra-fast scenes. In this paper, we present Ultra-Flow, an ultra-fast pipelined architecture for efficient optical flow estimation and refinement. Ultra-Flow utilizes binary neural networks to generate the robust feature map, on which hierarchical matching is directly performed. Therefore, multiple usages of neural networks at hierarchical levels can be avoided to achieve hardware efficiency in Ultra-Flow. Optimizations, including local flow regularization and enhanced matching, are further used to improve the throughput and refine the optical flow to obtain higher accuracy. Evaluation results show that, compared to state-of-the-art FPGA accelerators, Ultra-Flow achieves leading accuracy in the Middlebury sequences at ultra-fast processing speed up to 687.92 frames/s for 640<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11760_2025_4533_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> 480 pixel images.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A High Throughput Accelerator for Accurate Dense Optical Flow Estimation by Multi-scale Deep Feature Matching

  • 期刊论文

  • 发表日期：

    2025年7月30日

查看全文
  • Yehua Ling

摘要

Dense and accurate optical flow estimation is an important requirement for dynamic scene perception in autonomous systems. However, most of the existing FPGA accelerators are based on classic methods, which cannot deal with large displacements of moving objects in ultra-fast scenes. In this paper, we present Ultra-Flow, an ultra-fast pipelined architecture for efficient optical flow estimation and refinement. Ultra-Flow utilizes binary neural networks to generate the robust feature map, on which hierarchical matching is directly performed. Therefore, multiple usages of neural networks at hierarchical levels can be avoided to achieve hardware efficiency in Ultra-Flow. Optimizations, including local flow regularization and enhanced matching, are further used to improve the throughput and refine the optical flow to obtain higher accuracy. Evaluation results show that, compared to state-of-the-art FPGA accelerators, Ultra-Flow achieves leading accuracy in the Middlebury sequences at ultra-fast processing speed up to 687.92 frames/s for 640 \(\times \) × 480 pixel images.