Edge-Assisted Lightweight Region-of-Interest Extraction and Transmission for Vehicle Perception
GLOBECOM 2023 - 2023 IEEE Global Communications Conference（2023）
摘要
To enhance on-road environmental perception for autonomous driving, accurate and real-time analytics on high-resolution video frames generated from on-board cameras be-comes crucial. In this paper, we design a lightweight object location method based on class activation mapping (CAM) to rapidly capture the region of interest (RoI) boxes that contain driving safety related objects from on-board cameras, which can not only improve the inference accuracy of vision tasks, but also reduce the amount of transmitted data. Considering the limited on-board computation resources, the RoI boxes extracted from the raw image are offloaded to the edge for further processing. Considering both the dynamics of vehicle-to-edge communications and the limited edge resources, we propose an adaptive RoI box offloading algorithm to ensure prompt and accurate inference by adjusting the down-sampling rate of each box. Extensive experimental results on four high-resolution video streams demonstrate that our approach can effectively improve the overall accuracy by up to 16% and reduce the transmission demand by up to 49%, compared with other benchmarks.
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关键词
Computational Resources,Raw Images,Video Frames,Object Location,Vision Tasks,Task Accuracy,Inference Accuracy,Limited Computational Resources,Class Activation Maps,High-resolution Video,Onboard Camera,Convolutional Neural Network,Feature Maps,Object Detection,Long Short-term Memory,Resource Consumption,Mean Accuracy,Convolutional Neural Network Model,Redundant Information,Region Of Interest Extraction,Edge Server,Raw Frames,Lightweight Convolutional Neural Network,Target Object,Frames Per Second,Region Proposal,Autonomous Vehicles,Video Analysis,Inference Results
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