论文标题
总结在几个视频中测量的背景减法算法的性能
Summarizing the performances of a background subtraction algorithm measured on several videos
论文作者
论文摘要
存在许多背景减法算法，以检测视频中的运动。为了帮助它们进行比较，已经提出了与CDNET或LASIESTA等基真实数据的数据集。这些数据集在类别中组织视频，代表背景减法的典型挑战。由其作者推广的评估程序包括分别测量每个视频的性能指标，并平均在类别中，然后在类别之间，然后在类别之间进行层次层次，我们将其称为“摘要”的过程。尽管平均性能指标的总结是标准化评估程序的宝贵努力，但它没有理论上的理由，并且打破了汇总指标之间的内在关系。这导致解释不一致。在本文中，我们提出了一种理论方法，以总结多个视频的性能，以保留性能指标之间的关系。此外，我们给出公式和算法来计算汇总性能。最后，我们展示了我们在CDNET 2014上的观察结果。
There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promoted by their authors consists in measuring performance indicators for each video separately and to average them hierarchically, within a category first, then between categories, a procedure which we name "summarization". While the summarization by averaging performance indicators is a valuable effort to standardize the evaluation procedure, it has no theoretical justification and it breaks the intrinsic relationships between summarized indicators. This leads to interpretation inconsistencies. In this paper, we present a theoretical approach to summarize the performances for multiple videos that preserves the relationships between performance indicators. In addition, we give formulas and an algorithm to calculate summarized performances. Finally, we showcase our observations on CDNET 2014.
