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.

本研究提出了一个跨相机协作的分布式视频分析系统Argus，通过利用物体的时空关联，在多个相机的重叠视野中避免冗余的处理任务，动态排序相机和对象检查序列，并灵活分配工作负载，实现低延迟的分布式处理。该系统在三个真实世界的重叠相机数据集上的评估结果显示，相对于现有技术，Argus可以将对象标识的数量和端到端延迟分别减少多达7.13倍和2.19倍（相对于现有技术，分别减少了4.86倍和1.60倍），同时实现可比较的跟踪质量。

分布式智能摄像头上的视频分析实现跨摄像头协作