Compensating for camera translation in video eye-movement recordings by tracking a representative landmark selected automatically by a genetic algorithm.
Compensating for camera translation in video eye-movement recordings by tracking a representative landmark selected automatically by a genetic algorithm.
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通过跟踪由遗传算法自动选择的代表性地标来补偿视频眼动记录中的相机平移。
DOI：
10.1016/j.jneumeth.2008.09.010
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发表时间：
2009
影响因子：
3
通讯作者：
Shelhamer,Mark
中科院分区：
文献类型：
--
作者：
Karmali,Faisal;Shelhamer,Mark
在眼科和前庭研究中，通常使用视频或静态相机来获取关于眼球运动的数据。不幸的是，这样的数据往往被污染的面部相对于相机的不必要的运动，特别是在动态运动环境中的实验。我们开发了一种用于估计相机相对于高度可变形表面的运动的方法，特别是相机相对于面部和眼睛的运动。自动选择面部上的小矩形感兴趣区域（ROI）并在整个视频帧集合中跟踪，作为垂直相机平移的度量。具体目标是提出一个基于遗传算法的过程，该算法选择合适的ROI进行跟踪：其在相机图像内的平移准确地匹配相机的实际相对运动。我们发现，相关性，一个统计描述的时间序列的一大组的ROI，预测的准确性的ROI，并可以用来选择最好的ROI从一组。在遗传算法从一组中找到最佳ROI之后，它使用重组来形成新一代ROI，该新一代ROI继承来自上一代的ROI的属性。我们表明，该算法可以选择一个ROI，将估计相机平移，并确定眼睛正在寻找的方向，平均精度为0.75°，即使在120 mm的观看距离的2.5 mm的相机平移，这将导致11°的误差没有校正。
It is common in oculomotor and vestibular research to use video or still cameras to acquire data on eye movements. Unfortunately, such data are often contaminated by unwanted motion of the face relative to the camera, especially during experiments in dynamic motion environments. We develop a method for estimating the motion of a camera relative to a highly deformable surface, specifically the movement of a camera relative to the face and eyes. A small rectangular region of interest (ROI) on the face is automatically selected and tracked throughout a set of video frames as a measure of vertical camera translation. The specific goal is to present a process based on a genetic algorithm that selects a suitable ROI for tracking: one whose translation within the camera image accurately matches the actual relative motion of the camera. We find that co-correlation, a statistic describing the time series of a large group of ROIs, predicts the accuracy of the ROIs, and can be used to select the best ROI from a group. After the genetic algorithm finds the best ROIs from a group, it uses recombination to form a new generation of ROIs that inherit properties of the ROIs from the previous generation. We show that the algorithm can select an ROI that will estimate camera translation and determine the direction that the eye is looking with an average accuracy of 0.75°, even with camera translations of 2.5mm at a viewing distance of 120mm, which would cause an error of 11° without correction.