The considerable significance of Anomaly Detection (AD) problem has recently drawn the attention of many researchers. Consequently, the number of proposed methods in this research field has been increased steadily. AD strongly correlates with the important computer vision and image processing tasks such as image/video anomaly, irregularity and sudden event detection. More recently, Deep Neural Networks (DNNs) offer a high performance set of solutions, but at the expense of a heavy computational cost. However, there is a noticeable gap between the previously proposed methods and an applicable real-word approach. Regarding the raised concerns about AD as an ongoing challenging problem, notably in images and videos, the time has come to argue over the pitfalls and prospects of methods have attempted to deal with visual AD tasks. Hereupon, in this survey we intend to conduct an in-depth investigation into the images/videos deep learning based AD methods. We also discuss current challenges and future research directions thoroughly.
翻译:最近,异常探测(AD)问题的重要性已引起许多研究人员的注意,因此,这一研究领域拟议方法的数目稳步增加。DD与重要的计算机视觉和图像处理任务密切相关,例如图像/视频异常、异常和突发事件探测。最近,深神经网络提供了一套高性能的解决方案,但以高昂的计算成本为代价。然而,以前提出的方法与适用的实用实际语言方法之间有明显差距。关于AD这一持续具有挑战性的问题,特别是在图像和视频方面,人们提出的对AD这一持续具有挑战性的问题的关切,现在已经到了就图象化任务的缺陷和前景进行争论的时候了。在此调查中,我们打算对图像/视频的深度学习反倾销方法进行深入调查。我们还要透彻地讨论当前的挑战和今后的研究方向。