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Elixir: A system to enhance data quality for multiple analytics on a video stream
Elixir correctly detects 7. 1% (22, 068) and 5. 0% (15, 731) more cars, 94% (551) and 72% (478) more faces, and 670. 4% (4975) and 158. 6% (3507) more persons than the default-setting and time-sharing approaches, respectively.
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning
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.
Why is the video analytics accuracy fluctuating, and what can we do about it?
It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos.
Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning
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.
AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems
Millions of cameras at edge are being deployed to power a variety of different deep learning applications.
