Background subtraction with deep learning

Osprey Informatics Inc.有来自石油和天然气行业的大量客户对自动化监控感兴趣 户外场景。人工监控繁琐、容易出错且成本高昂。因此，鱼鹰信息公司正在寻找 以构建自动化和基于计算机视觉的解决方案。基于计算机视觉的第一步 对视频流的自动监控称为背景减法(BGS)，它可以找出运动 以及在被成像的场景中发生的时间变化。一旦执行了BGS，进一步的处理和 对现场的分析可以朝着自动监视的方向完成。现有的BGS解决方案有 事实证明，对于Opery处理的具有挑战性的数据集来说，这是无效的。数据集内的挑战包括 连续图像之间的长时间间隔，由于自然光照变化而导致的室外场景变化， 阴影的出现，以及由于刮风条件而导致的相机抖动。本研究的目标是构建一个BGS 解决方案，能够通过以下方式尽可能减轻上述不利因素的影响 使用计算机视觉中基于神经网络的最新工具。

Osprey Informatics Inc. has a large clientele from Oil and Gas sector interested in automated surveillance of outdoor scenes. Manual monitoring is tedious, error prone and expensive. Thus, Osprey Informatics is looking out to build automated and computer vision-based solutions. The very first step in a computer vision-based automated monitoring of a video stream is known as background subtraction (BGS), which finds out motion and temporal changes that occur in the scene being imaged. Once BGS is performed, further processing and analysis of the scene can be accomplished toward automated surveillance. Existing solutions for BGS have been proved ineffective for the challenging data sets Ospery deal with. Challenges within the data sets include long time lapse between consecutive images, outdoor scene changes due to natural lighting variations, appearance of shadows, and camera shakes due to windy conditions. The goal of this research is to build a BGS solution, which is able to mitigate the effects of the aforementioned adverse factors as much as possible by using the latest neural network-based tools from computer vision.