论文标题
通过结合无监督的背景减法算法来探索可实现的性能
An exploration of the performances achievable by combining unsupervised background subtraction algorithms
论文作者
论文摘要
背景减法（BGS）是在视频中执行运动检测的常见选择。每年都会发布数百种BGS算法，但是将它们结合起来以检测运动仍然没有探索。我们发现，组合策略可以利用大量可用的BGS算法，并为改进性能提供了重要的空间。在本文中，我们探讨了可以通过6种策略组合来实现的一组表演，即在CDNET 2014数据集中，在ROC空间和F1分数方面，在CDNET 2014数据集上，26个无监督的BGS算法的输出。所选的策略是大量策略的代表，包括确定性和非确定性的策略，即投票和学习。在我们的实验中，我们将结果与最新的IUTIS-5和CNN-SFC组合进行了比较，并报告了六个结论，其中包括单个算法的性能与通过组合可以实现的最佳性能之间存在重要差距。
Background subtraction (BGS) is a common choice for performing motion detection in video. Hundreds of BGS algorithms are released every year, but combining them to detect motion remains largely unexplored. We found that combination strategies allow to capitalize on this massive amount of available BGS algorithms, and offer significant space for performance improvement. In this paper, we explore sets of performances achievable by 6 strategies combining, pixelwise, the outputs of 26 unsupervised BGS algorithms, on the CDnet 2014 dataset, both in the ROC space and in terms of the F1 score. The chosen strategies are representative for a large panel of strategies, including both deterministic and non-deterministic ones, voting and learning. In our experiments, we compare our results with the state-of-the-art combinations IUTIS-5 and CNN-SFC, and report six conclusions, among which the existence of an important gap between the performances of the individual algorithms and the best performances achievable by combining them.
