结合无监督背景减除算法的性能探索
An exploration of the performances achievable by combining unsupervised background subtraction algorithms
摘要：
背景减除（BGS）是用于在视频中执行运动检测的常见选择。每年都有数百种BGS算法发布，但将它们结合起来检测运动在很大程度上仍未被探索。我们发现，组合策略允许利用大量可用的BGS算法，并为性能改进提供了显着的空间。在本文中，我们探索了通过6种策略实现的性能集，这些策略在CDnet 2014数据集上以像素方式结合了26种无监督BGS算法的输出，无论是在ROC空间还是在F1得分方面。所选择的策略代表了一个大的策略面板，包括确定性和非确定性的，投票和学习。在我们的实验中，我们比较我们的结果与国家的最先进的组合IUTIS-5和CNN-SFC，并报告六个结论，其中存在的一个重要的差距之间的性能的个别算法和最好的性能，通过结合它们。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
