Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work, we develop a method-agnostic weakly supervised a-contrario validation process, based on high dimensional statistical modeling of deep features, to reduce the number of false alarms of any change detection algorithm. We also raise the insufficiency of the conventionally used pixel-wise evaluation, as it fails to precisely capture the performance needs of most real applications. For this reason, we complement pixel-wise metrics with object-wise metrics and evaluate the impact of our approach at both pixel and object levels, on six methods and several sequences from different datasets. Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.

翻译：检测相关变化是视频监控中的基本问题。由于数据的高度变异性和变化标注的困难，无监督方法在该领域占据主导地位。使其实用化的关键问题之一在于降低虚警率。本文提出了一种基于深度特征高维统计建模的方法无关弱监督a-contrario验证流程，用于减少任意变化检测算法的虚警数量。此外，我们指出传统逐像素评估的不足——它无法精确反映大多数实际应用的性能需求。为此，我们在逐像素指标基础上补充了面向对象的评估指标，并在六种方法及多个数据集序列上，从像素级和对象级两个层面评估了本文方法的有效性。实验结果表明，所提出的a-contrario验证能够显著降低像素级和对象级两个层面的虚警数量。