@inproceedings{6b6e60728ff8408787f9eec6e268b091,
title = "Deep-Learning Based Multi-Object Detection and Tracking using Range-Angle Map in Automotive Radar Systems",
abstract = "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.",
keywords = "automotive radars, deep learning., Multiple object Tracking, object detection, range-angle map",
author = "Kim, {Ji He} and Lee, {Ming Chun} and Lee, {Ta Sung}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; null ; Conference date: 19-06-2022 Through 22-06-2022",
year = "2022",
doi = "10.1109/VTC2022-Spring54318.2022.9860725",
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",
address = "United States",
}