Semantics-Driven Cloud-Edge Collaborative Inference
Yuche Gao Beibei Zhang
With the proliferation of video data in smart city applications like
intelligent transportation, efficient video analytics has become crucial but
also challenging. This paper proposes a semantics-driven cloud-edge
collaborative approach for accelerating video inference, using license plate
recognition as a case study. The method separates semantics extraction and
recognition, allowing edge servers to only extract visual semantics (license
plate patches) from video frames and offload computation-intensive recognition
to the cloud or neighboring edges based on load. This segmented processing
coupled with a load-aware work distribution strategy aims to reduce end-to-end
latency and improve throughput. Experiments demonstrate significant
improvements in end-to-end inference speed (up to 5x faster), throughput (up to
9 FPS), and reduced traffic volumes (50% less) compared to cloud-only or
edge-only processing, validating the efficiency of the proposed approach. The
cloud-edge collaborative framework with semantics-driven work partitioning
provides a promising solution for scaling video analytics in smart cities.