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To identify dense and small-size pedestrians in surveillance systems,high-resolution cameras are widely deployed, where high-resolution images arecaptured 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 realtime. To address that, we propose Hode, an offloaded video analytic frameworkthat utilizes multiple edge nodes in proximity to expedite pedestrian detectionwith high-resolution inputs. Specifically, Hode can intelligently splithigh-resolution images into respective regions and then offload them todistributed edge nodes to perform pedestrian detection in parallel. Aspatio-temporal flow filtering method is designed to enable context-awareregion partitioning, as well as a DRL-based scheduling algorithm to allowaccuracy-aware load balance among heterogeneous edge nodes. Extensiveevaluation results using realistic prototypes show that Hode can achieve up to2.01% speedup with very mild accuracy loss.

Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via Parallel Edge Offloading