  To identify dense and small-size pedestrians in surveillance systems,
high-resolution cameras are widely deployed, where high-resolution images are
captured and delivered to off-the-shelf pedestrian detection models. However,
given the highly computation-intensive workload brought by the high resolution,
the resource-constrained cameras fail to afford accurate inference in real
time. To address that, we propose Hode, an offloaded video analytic framework
that utilizes multiple edge nodes in proximity to expedite pedestrian detection
with high-resolution inputs. Specifically, Hode can intelligently split
high-resolution images into respective regions and then offload them to
distributed edge nodes to perform pedestrian detection in parallel. A
spatio-temporal flow filtering method is designed to enable context-aware
region partitioning, as well as a DRL-based scheduling algorithm to allow
accuracy-aware load balance among heterogeneous edge nodes. Extensive
evaluation results using realistic prototypes show that Hode can achieve up to
2.01% speedup with very mild accuracy loss.
