Our Elixir is a system that enhances video analytics performance from multiple IoT cameras by adjusting camera settings through Multi-Objective Reinforcement Learning (MORL). In tests, Elixir significantly outperformed default settings and time-sharing methods, improving detection rates for cars, faces, persons, and license plates, addressing challenges in multi-analytical unit settings.
At the 9th International Conference on Internet of Things: Systems, Management and Security (IOTSMS 2022), we presented a novel reinforcement-learning system called APT, which dynamically adjusts camera parameters remotely via 5G networks. Experiments showed a significant accuracy improvement in video analytics, particularly a 42% enhancement in object detection, demonstrating APT's applicability across various tasks.
The paper presented at the 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022) discusses a framework called CamTuner, which dynamically adjusts non-automated (NAUTO) camera parameters to enhance the accuracy of analytics units in video analytics pipelines (VAP). The researchers highlight that environmental changes can severely impact the accuracy of insights derived from surveillance cameras. By utilizing SARSA reinforcement learning and incorporating an analytics quality estimator alongside a virtual camera to expedite training, CamTuner improves detection accuracy significantly—detecting 15.9% more individuals and increasing vehicle recognition rates in diverse scenarios—thereby enabling advanced applications like automatic vehicle collision prediction.
Giuseppe Coviello 