  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.
