Edge-Assisted Lightweight Region-of-Interest Extraction and Transmission for Vehicle Perception

Kavli Affiliate: Cheng Peng

| First 5 Authors: Yan Cheng, Peng Yang, Ning Zhang, Jiawei Hou,

| Summary:

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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