Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning
Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning
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DOI：
10.1145/3560905.3568527
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发表时间：
2021-07
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影响因子：
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通讯作者：
Sibendu Paul;Kunal Rao;G. Coviello;Murugan Sankaradas;Oliver Po;Y. C. Hu;S. Chakradhar
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作者：
Sibendu Paul;Kunal Rao;G. Coviello;Murugan Sankaradas;Oliver Po;Y. C. Hu;S. Chakradhar
在视频分析管道（VAP）中，在远程服务器上运行的对象检测和人脸识别等分析单元（AU）严重依赖监控摄像头来捕获高质量的视频流，以实现高准确性。现代IP摄像机具有大量的摄像机参数，这些参数直接影响视频流捕获的质量。虽然一些这样的参数，例如，曝光、对焦、白色平衡由相机内部自动调节，其余的则不是。我们将这样的相机参数表示为非自动化（Non-automated，NATIONAL）参数。在本文中，我们首先表明，环境条件的变化可以有显着的不利影响，从AU的见解的准确性，但这种不利影响可能会减轻动态调整的仙女座相机参数，以响应环境条件的变化。然后，我们提出了CamTuner，据我们所知，第一个框架，动态地适应仙女座相机参数，以优化在VAP中的AU的准确性，以应对环境条件的不利变化。CamTuner基于SARSA强化学习，它包含两个新的组件：一个轻量级的分析质量估计器和一个虚拟相机，可以大大加快离线RL训练。我们的控制实验和真实世界的VAP部署表明，与使用默认摄像机设置的VAP相比，CamTuner通过检测15.9%的额外人员和2.6%-4.2%的额外汽车来提高VAP的准确性（无任何误报）在一个大型企业停车场和9.7%的额外汽车在一个5G智慧交通路口的场景，这使得能够实现准确和可靠的自动车辆碰撞预测（AVCP）的新使用情况。CamTuner为显著提高视频分析准确性的新方法打开了大门，而不仅仅是通过完善深度学习模型进行增量改进。
In Video Analytics Pipelines (VAP), Analytics Units (AUs) such as object detection and face recognition running on remote servers critically rely on surveillance cameras to capture high-quality video streams in order to achieve high accuracy. Modern IP cameras come with a large number of camera parameters that directly affect the quality of the video stream capture. While a few of such parameters, e.g., exposure, focus, white balance are automatically adjusted by the camera internally, the remaining ones are not. We denote such camera parameters as non-automated (NAUTO) parameters. In this paper, we first show that environmental condition changes can have significant adverse effect on the accuracy of insights from the AUs, but such adverse impact can potentially be mitigated by dynamically adjusting NAUTO camera parameters in response to changes in environmental conditions. We then present CamTuner, to our knowledge, the first framework that dynamically adapts NAUTO camera parameters to optimize the accuracy of AUs in a VAP in response to adverse changes in environmental conditions. CamTuner is based on SARSA reinforcement learning and it incorporates two novel components: a light-weight analytics quality estimator and a virtual camera that drastically speed up offline RL training. Our controlled experiments and real-world VAP deployment show that compared to a VAP using the default camera setting, CamTuner enhances VAP accuracy by detecting 15.9% additional persons and 2.6%--4.2% additional cars (without any false positives) in a large enterprise parking lot and 9.7% additional cars in a 5G smart traffic intersection scenario, which enables a new usecase of accurate and reliable automatic vehicle collision prediction (AVCP). CamTuner opens doors for new ways to significantly enhance video analytics accuracy beyond incremental improvements from refining deep-learning models.