PFI-TT: Automatic Diagnosis of Video Quality Problems in Networked Camera Systems

这一创新-技术翻译合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力将使联网摄像系统能够制作质量更好的视频。该项目将设计并演示一个软件原型，该软件原型可以自动检测各种视频质量下降的情况，向操作人员提供质量下降原因的反馈，并推荐策略以提高接收视频的质量。所提出的原型将节省用于检查网络摄像机系统中的视频质量问题的人工。 该解决方案可以使公共安全中的各种视频用例受益，例如室内和室外监控、交通监控、访问控制和紧急操作。该项目的结果还可能影响智能健康监测、工业过程控制、智能和联网车辆以及虚拟/增强现实系统等应用中各种具有视觉传感的智能系统的设计。参加该项目的学生将接受技术商业化和创业方面的培训。该项目还将包括教育和外联活动，以扩大代表性不足的少数民族学生的参与，帮助建立计算领域的未来劳动力，拟议的项目旨在开发和演示联网摄像系统的视频质量评估和调整软件原型。多个因素导致联网摄像机系统中的视频质量的降级，诸如视频捕获期间的噪声或运动模糊、降级的分辨率、太多压缩以及不良网络条件。该项目将为复杂的网络摄像机系统开发一个系统的视频质量解决方案，包括传感，处理和通信组件。拟议的研究将侧重于：（1）调查系统设置和从实际的企业级监视系统生成的数据，（2）利用由人类用户和自动视频分析工具评估的视频质量的特征，（3）通过轻量级图像分析和视频比特流分析预测视频质量，（4）预测视频失真的类型并应用相应的质量恢复算法。拟议的技术将被集成到一个软件原型中，可供企业级相机系统的运营商使用。将进行迭代开发和评估任务，以确保拟议的原型系统与不同类型的摄像机，网络规模和连接，以及处理单元工作良好。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project will enable a networked camera system to produce videos with better quality. This project will design and demonstrate a software prototype that automatically detects various video quality degradation scenarios, provides feedback to human operators on the cause of quality degradation, and recommends strategies to improve the quality of received videos. The proposed prototype will save manual labor used for inspecting video quality problems in networked camera systems. The solution can benefit various video use cases in public safety, such as indoor and outdoor monitoring, traffic surveillance, access control, and emergency operations. The results from this project could also impact the design of a wide range of intelligent systems with visual sensing in applications like smart health monitoring, industrial process control, smart and connected vehicles, and virtual/augmented reality systems. The students participating in this project will receive training in technology commercialization and entrepreneurship. The project will also include educational and outreach activities to broaden the participation of under-represented minority students, helping to build the future workforce in the field of computing.The proposed project aims to develop and demonstrate a video quality assessment and adjustment software prototype for networked camera systems. Multiple factors cause the degradation of video quality in a networked camera system, such as noise or motion blur during video capturing, degraded resolution, too much compression, and bad network conditions. This project will develop a systematic video quality solution for complex networked camera systems with sensing, processing, and communication components. The proposed research will focus on: (1) investigating the system settings and the data generated from a practical enterprise-level surveillance system, (2) leveraging the characteristics of video quality evaluated by both human users and automatic video analytics tools, (3) predicting video quality through light-weight image analysis and video bitstream analysis, and (4) predicting the types of video distortion and applying corresponding quality restoration algorithms. The proposed technologies will be integrated in a software prototype that could be used by the operators of enterprise-level camera systems. Iterative development and evaluation tasks will be carried out to ensure that the proposed prototype works well for systems with different types of cameras, network scales and connections, and processing units.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.