Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check

Sheng Yao Kuan*, Jen Hao Cheng, Hsiang Wei Huang, Wenhao Chai, Cheng Yen Yang, Hugo Latapie, Gaowen Liu, Bing Fei Wu, Jenq Neng Hwang

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In the domain of autonomous driving, the integration of multi-modal perception techniques based on data from diverse sensors has demonstrated substantial progress. Effectively surpassing the capabilities of state-of-the-art single-modality detectors through sensor fusion remains an active challenge. This work leverages the respective advantages of cameras in perspective view and radars in Bird's Eye View (BEV) to greatly enhance overall detection and tracking performance. Our approach, Camera-Radar Associated Fusion Tracking Booster (CRAFTBooster) represents a pioneering effort to enhance radar-camera fusion in the tracking stage, contributing to improved 3D MOT accuracy. The superior experimental results on K-Radaar dataset, which exhibit 5-6% on IDF1 tracking performance gain, validate the potential of effective sensor fusion in advancing autonomous driving.

原文English
主出版物標題35th IEEE Intelligent Vehicles Symposium, IV 2024
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2125-2132
頁數8
ISBN(電子)9798350348811
DOIs
出版狀態Published - 2024
事件35th IEEE Intelligent Vehicles Symposium, IV 2024 - Jeju Island, 韓國
持續時間: 2 6月 20245 6月 2024

出版系列

名字IEEE Intelligent Vehicles Symposium, Proceedings
ISSN(列印)1931-0587
ISSN(電子)2642-7214

Conference

Conference35th IEEE Intelligent Vehicles Symposium, IV 2024
國家/地區韓國
城市Jeju Island
期間2/06/245/06/24

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