Video analytics are often performed as cloud services in edge settings,
mainly to offload computation, and also in situations where the results are not
directly consumed at the video sensors. Sending high-quality video data from
the edge devices can be expensive both in terms of bandwidth and power use. In
order to build a streaming video analytics pipeline that makes efficient use of
these resources, it is therefore imperative to reduce the size of the video
stream. Traditional video compression algorithms are unaware of the semantics
of the video, and can be both inefficient and harmful for the analytics
performance. In this paper, we introduce LtC, a collaborative framework between
the video source and the analytics server, that efficiently learns to reduce
the video streams within an analytics pipeline. Specifically, LtC uses the
full-fledged analytics algorithm at the server as a teacher to train a
lightweight student neural network, which is then deployed at the video source.
The student network is trained to comprehend the semantic significance of
various regions within the videos, which is used to differentially preserve the
crucial regions in high quality while the remaining regions undergo aggressive
compression. Furthermore, LtC also incorporates a novel temporal filtering
algorithm based on feature-differencing to omit transmitting frames that do not
contribute new information. Overall, LtC is able to use 28-35% less bandwidth
and has up to 45% shorter response delay compared to recently published state
of the art streaming frameworks while achieving similar analytics performance.

介绍了一种名为 LtC 的协作框架，通过在分析服务器上使用高级分析算法作为教师来训练轻量级的学生神经网络，从而在视频源端有效地减少视频流的大小，并使用基于特征差异的新颖时序过滤算法省略不包含新信息的帧，从而实现了 28-35% 的带宽减少和达到相似分析性能的 45% 短响应延迟。