Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing visual analytics systems for multi-camera streams are mostly limited to (i) per-camera processing and aggregation and (ii) workload-agnostic centralized processing architectures. In this paper, we present Argus, a distributed video analytics system with cross-camera collaboration on smart cameras. We identify multi-camera, multi-target tracking as the primary task of multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy identification tasks by leveraging object-wise spatio-temporal association in the overlapping fields of view across multiple cameras. We further develop a set of techniques to perform these operations across distributed cameras without cloud support at low latency by (i) dynamically ordering the camera and object inspection sequence and (ii) flexibly distributing the workload across smart cameras, taking into account network transmission and heterogeneous computational capacities. Evaluation of three real-world overlapping camera datasets with two Nvidia Jetson devices shows that Argus reduces the number of object identifications and end-to-end latency by up to 7.13x and 2.19x (4.86x and 1.60x compared to the state-of-the-art), while achieving comparable tracking quality.

翻译：重叠摄像头提供了从不同角度观察场景的独特机会，支持更先进、全面且鲁棒的分析。然而，现有面向多摄像头流的视觉分析系统主要局限于：(i) 单摄像头处理与聚合，以及(ii) 负载无关的集中式处理架构。本文提出Argus，一种支持智能摄像头跨摄像头协作的分布式视频分析系统。我们将多摄像头多目标跟踪定义为核心任务，并开发了一项新技术，通过利用多摄像头重叠视野中基于对象的时空关联，避免冗余且高计算量的识别任务。进一步地，我们提出一组技术以在无云端支持下实现低延迟分布式运算，具体包括：(i) 动态排序摄像头与目标检测顺序，以及(ii) 结合网络传输与异构计算能力，灵活分配跨智能摄像头的工作负载。基于两个Nvidia Jetson设备对三个真实场景重叠摄像头数据集的评估表明，Argus将目标识别次数与端到端延迟分别降低至7.13倍和2.19倍（与最先进方法相比为4.86倍和1.60倍），同时保持可比的跟踪质量。