  Enterprises are increasingly deploying large camera networks for video
analytics. Many target applications entail a common problem template: searching
for and tracking an object or activity of interest (e.g. a speeding vehicle, a
break-in) through a large camera network in live video. Such cross-camera
analytics is compute and data intensive, with cost growing with the number of
cameras and time. To address this cost challenge, we present ReXCam, a new
system for efficient cross-camera video analytics. ReXCam exploits spatial and
temporal locality in the dynamics of real camera networks to guide its
inference-time search for a query identity. In an offline profiling phase,
ReXCam builds a cross-camera correlation model that encodes the locality
observed in historical traffic patterns. At inference time, ReXCam applies this
model to filter frames that are not spatially and temporally correlated with
the query identity's current position. In the cases of occasional missed
detections, ReXCam performs a fast-replay search on recently filtered video
frames, enabling gracefully recovery. Together, these techniques allow ReXCam
to reduce compute workload by 8.3x on an 8-camera dataset, and by 23x - 38x on
a simulated 130-camera dataset. ReXCam has been implemented and deployed on a
testbed of 5 AWS DeepLens cameras.
