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。

MadEye: 自适应摄像头配置提升实时视频分析准确性

MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera  Configurations

Estimating absolute camera orientations is essential for attitude estimation
tasks. An established approach is to first carry out visual odometry (VO) or
visual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from the
camera poses (6 DOF) estimated by VO or V-SLAM. One drawback of this approach,
besides the redundancy in estimating full 6 DOF camera poses, is the dependency
on estimating a map (3D scene points) jointly with the 6 DOF poses due to the
basic constraint on structure-and-motion. To simplify the task of absolute
orientation estimation, we formulate the monocular rotational odometry problem
and devise a fast algorithm to accurately estimate camera orientations with
2D-2D feature matches alone. Underpinning our system is a new incremental
rotation averaging method for fast and constant time iterative updating.
Furthermore, our system maintains a view-graph that 1) allows solving loop
closure to remove camera orientation drift, and 2) can be used to warm start a
V-SLAM system. We conduct extensive quantitative experiments on real-world
datasets to demonstrate the accuracy of our incremental camera orientation
solver. Finally, we showcase the benefit of our algorithm to V-SLAM: 1) solving
the known rotation problem to estimate the trajectory of the camera and the
surrounding map, and 2)enabling V-SLAM systems to track pure rotational
motions.

通过 2D-2D 特征匹配技术，利用旋转平均算法设计一种可快速而又准确地估算单目相机旋转方向的系统，该系统通过维护一个视图图形并解决旋转问题来估算摄像机轨迹和地图，并驱动 V-SLAM 系统跟踪纯旋转运动。