Background subtraction with deep learning

通过深度学习进行背景扣除

• 批准号: 484880-2015
• 负责人: Ray, Nilanjan
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=584599
• 关键词: Background; subtraction; deep; learning

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.

Osprey Informatics Inc. 拥有大量来自石油和天然气行业的客户，对自动化监控感兴趣 室外场景。手动监控繁琐、容易出错且成本高昂。因此，Osprey Informatics 正在寻找 致力于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 视频流的自动监控称为背景减除 (BGS)，它可以找出运动 以及正在成像的场景中发生的时间变化。一旦执行 BGS，进一步处理和 可以完成场景分析以实现自动化监控。现有的 BGS 解决方案有 事实证明，对于 Ospery 处理的具有挑战性的数据集是无效的。数据集中的挑战包括 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、 由于大风条件，出现阴影和相机抖动。本研究的目标是建立一个BGS 的解决方案，能够尽可能减轻上述不利因素的影响 使用计算机视觉中最新的基于神经网络的工具。