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.

翻译：摄像头朝向（即旋转与变焦）决定了在给定场景中摄像头捕获的内容，而这又深刻影响着实时视频分析管线的精度。然而，现有分析方法并未触及这一关键的调节机制，仅局限于改变固定朝向下所捕获图像的编码、传输与分析方式。我们提出MadEye——一种摄像头-服务器协同系统，可自动且持续地调整摄像头朝向，以最大化当前工作负载与资源约束下的分析精度。为利用商用云台变焦（PTZ）摄像头实现此目标，MadEye集成了：（1）一种快速搜索算法，能够在海量朝向空间中有效探索，从而在每一时刻筛选出含有高价值信息的子集；（2）一种创新的知识蒸馏策略，可高效（仅利用摄像头资源）选择能最大化工作负载精度的朝向。面向多样化工作负载的实验表明，在同等资源消耗下MadEye可将精度提升2.9%-25.7%，或在保持相同精度时降低2-3.7倍的资源开销。