Block-matching optical flow for dynamic vision sensors: Algorithm and FPGA implementation
Block-matching optical flow for dynamic vision sensors: Algorithm and FPGA implementation
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DOI：
10.1109/iscas.2017.8050295
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发表时间：
2017-01
期刊：
影响因子：
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通讯作者：
Min Liu;T. Delbrück
中科院分区：
文献类型：
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作者：
Min Liu;T. Delbrück
快速、低功耗的光流计算在机器人领域具有潜在的应用价值。动态视觉传感器(DVS)事件摄像机输出速度快、输出稀疏、动态范围大，但传统的算法都是基于帧的，不能直接用于基于事件的摄像机。以前的方法的DV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。受运动估计方法的启发，提出了一种新的基于块匹配的DVS算法。该算法在软件上实现，并在现场可编程门阵列上实现。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%。OF可以在50 MHz时钟的FPGA上计算，每个事件0.2 us(11个时钟周期)，比在台式PC上运行的Java软件实现快20倍。样本数据表明，该方法适用于边缘为主、特征稀疏、纹理密集的场景。
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.