System Design of Maxim
Real-time video analytics (VA) with requirement of high accuracy necessitates intensive bandwidth resource consumption, calling for an adaptive streaming configuration strategy to strike a balance in VA pipelines. Existing works however suffer from a key limitation: the profiling-based strategy not only wastes unnecessary resources with the golden configuration transmission but also is trapped into only coarse-grained adaptation due the contradiction between profiling granularity and the profiling cost.
In this paper, we for the first time reveal the correlation between the video dynamics and motion degree and high light the limitation of traditional profiling-based strategy. We then propose Maxim, the first learning-based framework that solves the fine-grained VA configuration adaptation problem employing a novel deep reinforcement learning-based methodology without using any golden configuration in inference stage. Maxim could optimize the trade-off between resources cost and inference accuracy. Besides, Maxim also employs an enhanced cross-camera collaboration based on spatial and temporal correlation among cameras, which further improves robustness and performance in a large camera network. Extensive experiments confirm the superiority of our work compared with SOTA works, with a 76.6% improvement in comprehensive.
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