Deep-Learning Based Multi-Object Detection and Tracking using Range-Angle Map in Automotive Radar Systems

Ji He Kim, Ming Chun Lee, Ta Sung Lee

研究成果: Conference contribution同行評審

摘要

In this paper, a machine learning-based object detection and tracking approach in radar system is proposed via using the range-angle map as the input. Specifically, by using the You Only Look Once (YOLO) for object detection and Deep Simple Online and Realtime Tracking (D-SORT) for tracking, the proposed approach can improve the detection and tracking performance, reducing the parameters needed to be manually selected, and providing more relevant information, such as the shape, size, and category of the object. We conduct the realistic simulations to evaluate the proposed approach. Results show that our proposed approach can outperform the conventional radar processing approach in terms of detection and tracking performance. Furthermore, results indicate that the object categorization of the proposed approach is accurate.

原文English
主出版物標題2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665482431
DOIs
出版狀態Published - 2022
事件95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland
持續時間: 19 6月 202222 6月 2022

出版系列

名字IEEE Vehicular Technology Conference
2022-June
ISSN(列印)1550-2252

Conference

Conference95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring
國家/地區Finland
城市Helsinki
期間19/06/2222/06/22

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