Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks

The problem of region of interest (RoI) extraction and transmission of video frames captured in edge-assisted unmanned aerial vehicle (UAV) systems was investigated to improve the inference performance of patrolling tasks. Due to the limited UAV onboard computational resources, a lightweight RoI extraction method based on class activation mapping (CAM) was proposed, which was able to rapidly locate areas containing patrolling targets. Those RoIs were then transmitted to edge servers for further processing. To address the challenges from dynamic UAV trajectories and fluctuating network conditions, the RoIs collected by UAVs were properly choosen through an adaptive RoI box selection algorithm, followed by adaptive configuration of quantization parameters (QP) of video codec, in order to further compress the transmitted data volume. A joint optimization problem was thus formulated for RoI box selection and adaptive coding configuration, which was solved via a heuristic algorithm. Experimental results demonstrate that, the proposed approach can effectively improve the detection accuracy of patrolling tasks, reduce data transmission volume, and significantly lower system latency, indicating great potential in UAV-based patrolling applications.