The high-accuracy and resource-intensive deep neural networks (DNNs) have
been widely adopted by live video analytics (VA), where camera videos are
streamed over the network to resource-rich edge/cloud servers for DNN
inference. Common video encoding configurations (e.g., resolution and frame
rate) have been identified with significant impacts on striking the balance
between bandwidth consumption and inference accuracy and therefore their
adaption scheme has been a focus of optimization. However, previous
profiling-based solutions suffer from high profiling cost, while existing deep
reinforcement learning (DRL) based solutions may achieve poor performance due
to the usage of fixed reward function for training the agent, which fails to
craft the application goals in various scenarios. In this paper, we propose
ILCAS, the first imitation learning (IL) based configuration-adaptive VA
streaming system. Unlike DRL-based solutions, ILCAS trains the agent with
demonstrations collected from the expert which is designed as an offline
optimal policy that solves the configuration adaption problem through dynamic
programming. To tackle the challenge of video content dynamics, ILCAS derives
motion feature maps based on motion vectors which allow ILCAS to visually
``perceive'' video content changes. Moreover, ILCAS incorporates a cross-camera
collaboration scheme to exploit the spatio-temporal correlations of cameras for
more proper configuration selection. Extensive experiments confirm the
superiority of ILCAS compared with state-of-the-art solutions, with 2-20.9%
improvement of mean accuracy and 19.9-85.3% reduction of chunk upload lag.

提出了基于模仿学习的配置自适应视频分析流系统 ILCAS，通过收集专家演示来训练代理人，解决了配置适应问题，并利用动态编程解决视频内容动态性挑战，同时结合跨摄像头协作方案以选择更合适的配置，实验结果证实了 ILCAS 相对于最先进解决方案的卓越性，平均准确度提高 2-20.9％，块上传延迟降低 19.9-85.3％。

ILCAS：基于模仿学习的适应性配置流式传输，用于带有跨摄像机协作的实时视频分析

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for  Live Video Analytics with Cross-Camera Collaboration

Camera orientations (i.e., rotation and zoom) govern the content that a
camera captures in a given scene, which in turn heavily influences the accuracy
of live video analytics pipelines. However, existing analytics approaches leave
this crucial adaptation knob untouched, instead opting to only alter the way
that captured images from fixed orientations are encoded, streamed, and
analyzed. We present MadEye, a camera-server system that automatically and
continually adapts orientations to maximize accuracy for the workload and
resource constraints at hand. To realize this using commodity pan-tilt-zoom
(PTZ) cameras, MadEye embeds (1) a search algorithm that rapidly explores the
massive space of orientations to identify a fruitful subset at each time, and
(2) a novel knowledge distillation strategy to efficiently (with only camera
resources) select the ones that maximize workload accuracy. Experiments on
diverse workloads show that MadEye boosts accuracy by 2.9-25.7% for the same
resource usage, or achieves the same accuracy with 2-3.7x lower resource costs.

MadEye 是一个使用 PTZ 摄像机的摄像机 - 服务器系统，它含有搜索和知识蒸馏算法，使得摄像机能够自动地和不断地调整朝向以最大化负载准确性，实验结果表明，MadEye 在相同的资源使用情况下，将准确度提高了 2.9-25.7% ，或以 2-3.7 倍的资源成本实现相同的准确度。