Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera (i.e CCD cameras, omnidirectional cameras, …), foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

基于视频的计算机视觉应用程序通常需要在第一步中检测运动对象。然后应用背景减法以分离背景和前景。在文献中，背景消减无疑是计算机视觉中研究最多的领域之一，提供了大量出版物。他们中的大多数人都关注数学和机器学习模型的应用，以使其更强大地应对视频中遇到的挑战。但是，最终目标是研究中开发的背景扣除方法可用于交通监控等实际应用中。但是从文献来看，我们可以指出，实际应用中使用的当前方法与基础研究中使用的当前方法之间通常存在差距。此外，在大规模数据集中评估的视频并不完整，因为它们仅涵盖了实际应用中遇到的全部挑战的一部分。在这种情况下，我们尝试对使用背景减法的实际应用程序提供尽可能详尽的调查，以便确定实际遇到的实际挑战，当前使用的背景模型并提供未来的方向。因此，从摄像机（即CCD摄像机，全向摄像机等），前景物体和环境方面研究了挑战。此外，我们确定了在这些应用程序中有效使用的背景模型，以便在鲁棒性，时间和内存要求方面找到潜在的可用的最新背景模型。