发表机构
Australian National University; School of Engineering（澳大利亚国立大学; 工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究图像跟踪问题，提出用事件相机进行高性能跟踪的等变滤波器设计，利用AEB跟踪器提取测量值，经等变滤波器计算平移和旋转，含等效测量更新步骤，实验评估表明能为高速移动特征提供平滑跟踪。
AI 中文摘要
图像跟踪是估计场景的运动图像与原始参考图像之间变换的问题。该问题在自动驾驶车辆或机器人控制中很重要，图像编码了相机或环境运动的信息，在纯计算机视觉应用中也很重要。本文提出一种等变滤波器设计，用于使用事件相机对平面图像变换进行高性能跟踪。该设计利用异步事件Blob（AEB）跟踪器从原始事件流中提取特征位置测量值，并使用特殊欧几里得群对称性的等变滤波器计算仿射图像平移和旋转。等变滤波器包含等效测量更新步骤，使AEB跟踪器提供的（高度时间相关的）特征位置测量值去相关。我们使用涉及一般和快速旋转运动的两个数据集对设计进行实验评估。我们将结果与直接优化（从原始Blob轨迹估计相对变换）以及克服数据相关性的协方差交集方法进行基准测试。我们的设计为图像平面上每秒移动高达7000像素的特征提供平滑图像跟踪。
英文摘要
Image tracking is the problem of estimating the transformation that relates a moving image of a scene to an original reference image. The problem is important in control of autonomous vehicles or robots, where the image encodes information about the motion of the camera or environment, as well as in pure computer vision applications. In this paper, we present an equivariant filter design for high performance tracking of planar image transformations using an event camera. The design exploits the Asynchronous Event Blob (AEB) tracker (Wang et al., 2024) to extract feature-position measurements from the raw event stream, and an equivariant filter to compute an affine image translation and rotation using the special Euclidean group symmetry. The equivariant filter incorporates an equivalent-measurement update step that de-correlates the (highly temporally correlated) feature-position measurements provided by the AEB tracker. We evaluate the design experimentally using two datasets involving general and fast rotational motion. We benchmark results against direct optimisation (estimating the relative transformation from the raw blob tracks), and a covariance intersection approach for overcoming data correlation. Our design provides smooth image tracking for features moving up to 7000 pixels per second on the image plane.
