Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor.
Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor.
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DOI：
10.3389/fnins.2016.00176
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发表时间：
2016
影响因子：
4.3
通讯作者：
Delbruck T
中科院分区：
文献类型：
--
作者：
Rueckauer B;Delbruck T
在这项研究中，我们比较了九种光流算法，它们根据精度和计算代价局部测量垂直于边缘的流。与传统的基于帧的运动流算法不同，我们的开源实现基于来自神经形态动态视觉传感器(DVS)的地址事件来计算光流。在这个基准测试中，我们创建了一个由240×180像素动态和有源像素视觉传感器(Davis)记录的两个合成样本和三个真实样本的数据集。此数据集包含来自DVS的事件以及常规帧，以支持测试最先进的基于帧的方法。我们介绍了一种新的地面真理来源：在视觉传感器绕三个摄像机轴旋转的特殊情况下，可以使用与Davis相机集成的惯性测量单元的陀螺数据来估计真正的光流。这提供了一个基本事实，我们可以将通过运动提示测量光流的算法与之进行比较。对误差源的分析导致使用不稳定周期、更精确的数值导数和Savitzky-Golay滤波器来实现精度的显著提高。与原始实现相比，我们对最近发布的两种算法的纯Java实现降低了高达29%的计算成本。本文介绍的两种算法在精度相同或更高的情况下，与原始实现相比，处理速度进一步提高了10倍。在台式PC上，它们通过戴维斯摄像头记录的密集自然输入实时运行。
In this study we compare nine optical flow algorithms that locally measure the flow normal to edges according to accuracy and computation cost. In contrast to conventional, frame-based motion flow algorithms, our open-source implementations compute optical flow based on address-events from a neuromorphic Dynamic Vision Sensor (DVS). For this benchmarking we created a dataset of two synthesized and three real samples recorded from a 240 × 180 pixel Dynamic and Active-pixel Vision Sensor (DAVIS). This dataset contains events from the DVS as well as conventional frames to support testing state-of-the-art frame-based methods. We introduce a new source for the ground truth: In the special case that the perceived motion stems solely from a rotation of the vision sensor around its three camera axes, the true optical flow can be estimated using gyro data from the inertial measurement unit integrated with the DAVIS camera. This provides a ground-truth to which we can compare algorithms that measure optical flow by means of motion cues. An analysis of error sources led to the use of a refractory period, more accurate numerical derivatives and a Savitzky-Golay filter to achieve significant improvements in accuracy. Our pure Java implementations of two recently published algorithms reduce computational cost by up to 29% compared to the original implementations. Two of the algorithms introduced in this paper further speed up processing by a factor of 10 compared with the original implementations, at equal or better accuracy. On a desktop PC, they run in real-time on dense natural input recorded by a DAVIS camera.