Video sequence analysis systems must be able to operate for long periods of time and they must attempt that the different events that can affect the quality of the input data do not diminish the performance of the system to an excessive extent. In this work a method called Probabilistic Mixture ofDeeply Autoencoded Patch Features (PMDAPF) is proposed. A Deep Autoencoder is the cornerstone of the methodology for robust background modeling and foreground detection that is presented in this document. Its purpose is to obtain a reduced set of significant features from each patch belonging to one of the several shifted tilings of the video frames. Then, a probabilistic model is responsible for determining whether the whole patch belongs to the background or not. Foreground pixel detection takes into account the information of all patches in which the pixel is included. The robustness of the proposal, as well as its suitability to the uninterrupted analysis and processing of visual information, is reflected in the experiments, in which the performance of the proposed system is affected slightly whereas those of the classic methods are degraded drastically.

中文翻译：

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通过深度自动编码器学习的补丁特征的概率建模进行背景扣除

视频序列分析系统必须能够长时间运行，并且它们必须尝试可能影响输入数据质量的不同事件不会在很大程度上降低系统性能。在这项工作中，提出了一种称为深度自动编码补丁特征的概率混合（PMDAPF）的方法。深度自动编码器是本文档中介绍的用于可靠的背景建模和前景检测的方法的基石。其目的是从属于视频帧的多个平铺瓷砖之一的每个补丁中获得一组减少的重要特征。然后，概率模型负责确定整个补丁是否属于背景。前景像素检测考虑了其中包含像素的所有面片的信息。在实验中反映了该建议的鲁棒性及其对可视信息的不间断分析和处理的适用性，在实验中，所提议系统的性能受到轻微影响，而传统方法的性能则大大降低。