动态视觉传感器的块匹配光流算法及FPGA实现
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
摘要：
快速、低功耗的光流（OF）计算在机器人技术中具有潜在的应用价值。动态视觉传感器（DVS）事件相机产生快速和稀疏的输出，并且具有高动态范围，但是传统的OF算法是基于帧的，并且不能直接与基于事件的相机一起使用。先前的DVS OF方法不能很好地与密集纹理输入一起工作，并且被设计用于在逻辑电路中实现。本文从MPEG视频压缩中的运动估计方法中得到启发，提出了一种新的基于块匹配的DVS OF算法。该算法在软件和FPGA上实现。对于每个事件，它将运动方向计算为9个方向之一。运动的速度由采样间隔设置。结果表明，与以往的方法相比，平均角度误差可提高30%。在FPGA上以50 MHz的时钟频率计算出的OF值为0.2us/事件（11个时钟周期），比在PC上运行的Java软件快20倍。样本数据表明，该方法的工作原理的边缘，稀疏的功能，和密集的纹理占主导地位的场景。
英文摘要：
Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30\% compared with previous methods. The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
