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.

鱼鹰信息公司（Osprey Informatics Inc.）有大量来自石油和天然气行业的客户，他们对石油和天然气的自动监控感兴趣