APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning
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DOI：
10.1109/iotsms58070.2022.10062226
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发表时间：
2022-11
期刊：
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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文献类型：
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作者：
Sibendu Paul;Kunal Rao;G. Coviello;Murugan Sankaradas;Oliver Po;Y. C. Hu;S. Chakradhar
摄像机越来越多地部署在全球的城市、企业和道路上，以实现公共安全、智能交通、零售、医疗保健和制造业的许多应用。通常，在最初部署摄像头后，这些摄像头周围的环境条件和场景会发生变化，我们的实验表明，这些变化会对视频分析的准确性产生不利影响。这是因为摄像机参数设置虽然在部署时是最佳的，但由于摄像机周围的环境条件和场景在操作期间发生变化，因此并不是高质量视频捕获的最佳设置。捕获低质量的视频会对分析的准确性产生不利影响。为了减轻洞察力准确性的损失，我们提出了一种新颖的基于学习的系统APT，该系统可以动态地远程（通过5G网络）调整摄像机参数，以确保高质量的视频捕获，从而减轻视频分析准确性的任何损失。因此，当环境条件或场景内容改变时，这种调整恢复洞察的准确性。APT使用强化学习，以无参考感知质量估计作为奖励函数。我们进行了广泛的真实世界的实验，我们同时部署了两个摄像头并排俯瞰企业停车场（一个摄像头只有供应商建议的默认设置，而另一个摄像头在操作过程中由APT动态调整）。我们的实验表明，由于APT的动态调整，分析洞察力在一天中的任何时候都始终如一地更好：对象检测视频分析应用程序的准确性平均提高了42%。由于我们的奖励功能独立于任何分析任务，APT可以很容易地用于不同的视频分析任务。
Cameras are increasingly being deployed in cities, enterprises and roads world-wide to enable many applications in public safety, intelligent transportation, retail, healthcare and manufacturing. Often, after initial deployment of the cameras, the environmental conditions and the scenes around these cameras change, and our experiments show that these changes can adversely impact the accuracy of insights from video analytics. This is because the camera parameter settings, though optimal at deployment time, are not the best settings for good-quality video capture as the environmental conditions and scenes around a camera change during operation. Capturing poor-quality video adversely affects the accuracy of analytics. To mitigate the loss in accuracy of insights, we propose a novel, reinforcement-learning based system APT that dynamically, and remotely (over 5G networks), tunes the camera parameters, to ensure a high-quality video capture, which mitigates any loss in accuracy of video analytics. As a result, such tuning restores the accuracy of insights when environmental conditions or scene content change. APT uses reinforcement learning, with no-reference perceptual quality estimation as the reward function. We conducted extensive real-world experiments, where we simultaneously deployed two cameras side-by-side overlooking an enterprise parking lot (one camera only has manufacturer-suggested default setting, while the other camera is dynamically tuned by APT during operation). Our experiments demonstrated that due to dynamic tuning by APT, the analytics insights are consistently better at all times of the day: the accuracy of object detection video analytics application was improved on average by ∼ 42%. Since our reward function is independent of any analytics task, APT can be readily used for different video analytics tasks.