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

创新伙伴关系-技术转化（PFI-TT）项目的更广泛影响/商业潜力将使网络摄像系统能够制作质量更高的视频。该项目将设计并演示一个软件原型，该原型可以自动检测各种视频质量下降场景，向人工操作员提供质量下降原因的反馈，并建议提高接收视频质量的策略。提出的原型将节省用于检查网络摄像机系统中视频质量问题的人工劳动。该解决方案可用于公共安全领域的各种视频用例，如室内和室外监控、交通监控、访问控制和应急操作。该项目的研究结果还可能影响智能健康监测、工业过程控制、智能互联汽车以及虚拟/增强现实系统等应用中具有视觉传感的广泛智能系统的设计。参与该项目的学生将接受技术商业化和创业培训。该项目还将包括教育和外展活动，以扩大代表性不足的少数民族学生的参与，帮助在计算机领域建立未来的劳动力。拟议的项目旨在开发和演示网络摄像机系统的视频质量评估和调整软件原型。在网络摄像机系统中，导致视频质量下降的因素有很多，例如视频捕获过程中的噪声或运动模糊、分辨率下降、压缩过多、网络条件差等。该项目将为具有传感、处理和通信组件的复杂网络摄像机系统开发系统视频质量解决方案。建议的研究将侧重于：(1)调查系统设置和实际企业级监控系统生成的数据；(2)利用人类用户和自动视频分析工具评估的视频质量特征；(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)预测视频失真类型并应用相应的质量恢复算法。提议的技术将集成在一个软件原型中，供企业级相机系统的运营商使用。将执行迭代开发和评估任务，以确保所提议的原型在具有不同类型的摄像机、网络规模和连接以及处理单元的系统中工作良好。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.