  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.
