Owl: A Pre-and Post-processing Framework for Video Analytics in Low-light Surroundings

The low-light environment is an integral surrounding in real-world video analytic applications. Conventional wisdom claims that in order to adapt to the extensive computation requirement of the analytics model and achieve high inference accuracy, the overall pipeline should leverage a client-to-cloud framework that designs a cloud-based inference with on-demand video streaming. However, we show that due to the amplified noise, directly streaming the video in low-light scenarios can introduce significant bandwidth inefficiency.In this paper, we propose Owl, an intelligent framework to optimize the bandwidth utilization and inference accuracy for the low-light video analytic pipeline. The core idea of Owl is two-fold: on the one hand, we will deploy a light-weighted pre-processing module before transmission, through which we will get the denoised video and significantly reduce the transmitted data; on the other hand, we recover the information from the denoised video via an enhancement module in the server-side. Specifically, through well-designed training mechanism and content representation technique, Owl can dynamically select the best configuration for time-varying videos. Experiments with a variety of datasets and tasks show that Owl achieves significant bandwidth benefits, while consistently optimizing the inference accuracy.