I-Corps: Control for Visual Scene Perception

I-Corps：视觉场景感知控制

• 批准号: 1934303
• 负责人: Silvia Ferrari
• 金额: $ 5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934303&HistoricalAwards=false
• 关键词: Corps; Control; Visual; Scene; Perception

The broader impact/commercial potential of the proposed I-Corps project consists of significantly increased safety and control for real-world applications of computer vision and mobile robotics. The first application in the optimal monitoring and control of smart environments will impact the safety and productivity of many industries, such as video surveillance, access control, and smart buildings/cities. Computer-vision software applications will impact industries such as autonomous automobiles by predicting actions of nearby pedestrians, animals, or other vehicles. Automated video processing and recognition software will potentially reduce the impact of catastrophes such as terrorist attacks or mass shootings via intent prediction and anomaly detection available through the proposed video analytics software. Additionally, in a different application the proposed solution may be used to optimize a building's energy efficiency and reduce building operating costs.This I-Corps project further develops a platform for autonomous visual scene perception and feedback control. Autonomous real-time perception and predictive control will dramatically increase the safety and efficiency across several potential applications by directing a human operator's attention to a situation or automatically applying changes to the sensor field of view or controllable environmental conditions such as lighting or temperature. The unique deep learning Bayesian optimization framework does not require prior knowledge of the scene in which it is deployed and instead learns the scene representation over time. The project ties together machine learning, estimation, and control systems. Proof of concept testing has been successfully completed in real-time vehicles equipped with cameras and using deep learning in the loop for autonomous control of the sensor field of view.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

拟议的i-Corps项目的更广泛影响/商业潜力包括显著提高计算机视觉和移动机器人在现实世界中的应用的安全性和可控性。智能环境优化监控的第一个应用将影响视频监控、门禁和智能建筑/城市等许多行业的安全和生产力。计算机视觉软件应用程序将通过预测附近行人、动物或其他车辆的行为来影响自动驾驶汽车等行业。自动化视频处理和识别软件将通过拟议的视频分析软件提供的意图预测和异常检测，潜在地减少恐怖袭击或大规模枪击等灾难的影响。此外，在不同的应用中，建议的解决方案可以用于优化建筑的能效和降低建筑运营成本。这个i-Corps项目进一步开发了一个自主视觉场景感知和反馈控制的平台。自主实时感知和预测控制将极大地提高几个潜在应用的安全性和效率，方法是将人工操作员的注意力引导到某个情况，或自动对传感器视野或可控环境条件(如照明或温度)进行更改。独特的深度学习贝叶斯优化框架不需要部署它的场景的先验知识，而是随着时间的推移学习场景表示。该项目将机器学习、评估和控制系统联系在一起。概念验证测试已经在配备摄像头的实时车辆上成功完成，并使用回路中的深度学习来自主控制传感器视野。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。