  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-MOT

Computer Vision and Pattern Recognition