@inproceedings{d8855f0c98f4489792b2b5a940e5617c,
title = "Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check",
abstract = "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.",
author = "Kuan, {Sheng Yao} and Cheng, {Jen Hao} and Huang, {Hsiang Wei} and Wenhao Chai and Yang, {Cheng Yen} and Hugo Latapie and Gaowen Liu and Wu, {Bing Fei} and Hwang, {Jenq Neng}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 35th IEEE Intelligent Vehicles Symposium, IV 2024 ; Conference date: 02-06-2024 Through 05-06-2024",
year = "2024",
doi = "10.1109/IV55156.2024.10588514",
language = "English",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2125--2132",
booktitle = "35th IEEE Intelligent Vehicles Symposium, IV 2024",
address = "美國",
}