The KITTI Vision Benchmark Suite

Firstly, a new multi-object tracking framework is
proposed in this paper based on multi-modal
fusion. By integrating object detection and multi-
object tracking into the same model, this
framework avoids the complex data association
process in the classical TBD paradigm, and
requires no additional training. Secondly,
confidence of historical trajectory regression is
explored, possible states of a trajectory in the
current frame (weak object or strong object) are
analyzed and a confidence fusion module is
designed to guide non-maximum suppression of
trajectory and detection for ordered association.
Finally, extensive experiments are conducted on
the KITTI and Waymo datasets. The results show
that the proposed method can achieve robust
tracking by using only two modal detectors and it
is more accurate than many of the latest TBD
paradigm-based multi-modal tracking methods. The
source codes of the proposed method are available
at https://github.com/wangxiyang2022/YONTD-

TBD

@article{wang2024multi,
title={A Multi-Modal Fusion-Based 3D Multi-
Object Tracking Framework with Joint Detection},
author={Wang, Xiyang and Fu, Chunyun and He,
Jiawei and Huang, Mingguang and Meng, Ting and
Zhang, Siyu and Zhou, Hangning and Xu, Ziyao and
Zhang, Chi},
journal={IEEE Robotics and Automation Letters},
year={2024},
publisher={IEEE}
}