Space-Time Tradeoffs in Photo Sequencing

Tali Basha, Y. Moses, S. Avidan
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引用次数: 24

Abstract

Photo-sequencing is the problem of recovering the temporal order of a set of still images of a dynamic event, taken asynchronously by a set of uncalibrated cameras. Solving this problem is a first, crucial step for analyzing (or visualizing) the dynamic content of the scene captured by a large number of freely moving spectators. We propose a geometric based solution, followed by rank aggregation to the photo-sequencing problem. Our algorithm trades spatial certainty for temporal certainty. Whereas the previous solution proposed by [4] relies on two images taken from the same static camera to eliminate uncertainty in space, we drop the static-camera assumption and replace it with temporal information available from images taken from the same (moving) camera. Our method thus overcomes the limitation of the static-camera assumption, and scales much better with the duration of the event and the spread of cameras in space. We present successful results on challenging real data sets and large scale synthetic data (250 images).
照片排序中的时空权衡
照片排序是恢复动态事件的一组静止图像的时间顺序的问题,这些图像是由一组未校准的相机异步拍摄的。解决这个问题是分析(或可视化)大量自由移动的观众捕捉到的场景动态内容的第一步,也是至关重要的一步。我们提出了一个基于几何的解决方案,其次是秩聚合的照片排序问题。我们的算法将空间确定性换成了时间确定性。先前[4]提出的解决方案依赖于从同一静态相机拍摄的两幅图像来消除空间中的不确定性,而我们放弃了静态相机的假设,并将其替换为从同一(移动)相机拍摄的图像中获得的时间信息。因此,我们的方法克服了静态摄像机假设的局限性,并且随着事件的持续时间和摄像机在空间中的分布而更好地缩放。我们在具有挑战性的真实数据集和大规模合成数据(250张图像)上展示了成功的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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