@inproceedings{fd832767f7d942afb39454a9b2172b06,
title = "Object Recognition Network using Continuous Roadside Cameras",
abstract = "For the purpose of establishing a digital twin society, this paper proposes an object recognition network architecture using high-definition (HD) camera images. HD camera is installed to the edge computing resource in Road-Side Unit (RSU). The edge computing resource detects vehicles by object detection and Re-ID function based on deep learning. Its key feature is to reduce the amount of network data traffic by extracting vehicle images and directly transferring them to neighboring RSUs, i.e. vehicle identification and tracking can be achieved in the localized backhaul network. This paper experimentally verifies its fundamental feasibility using outdoor testbed field. The proposed framework can significantly reduce the data traffic by more than 90\% while maintaining vehicle Re-ID accuracy.",
keywords = "Edge Computing, ITS, Image Processing, Proof-of-Concept, RSU, Re-Identification of Vehicle, Traffic Reduction, V2X",
author = "Gunhee Cho and Yusuke Shinyama and Jin Nakazato and Kazuki Maruta and Kei Sakaguchi",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring ; Conference date: 19-06-2022 Through 22-06-2022",
year = "2022",
doi = "10.1109/VTC2022-Spring54318.2022.9860677",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings",
}