The KITTI Vision Benchmark Suite

This paper proposes a robust and fast camera-LiDAR
fusion-based MOT method that achieves a good
trade-off between accuracy and speed. Relying on
the characteristics of camera and LiDAR sensors,
an effective deep association mechanism is
designed and embedded in the proposed MOT method.
This association mechanism realizes tracking of an
object in a 2D domain when the object is far away
and only detected by the camera, and updating of
the 2D trajectory with 3D information obtained
when the object appears in the LiDAR field of view
to achieve a smooth fusion of 2D and 3D
trajectories. Extensive experiments based on the
KITTI dataset indicate that our proposed method
presents obvious advantages over the state-of-the-
art MOT methods in terms of both tracking accuracy
and processing speed. Code
available:https://github.com/wangxiyang2022/DeepFu
sionMOT

See the code for details.

@ARTICLE{9810346, author={Wang, Xiyang and Fu,
Chunyun and Li, Zhankun and Lai, Ying and He,
Jiawei}, journal={IEEE Robotics and Automation
Letters}, title={DeepFusionMOT: A 3D Multi-Object
Tracking Framework Based on Camera-LiDAR Fusion with
Deep Association}, year={2022}, volume={},
number={}, pages={1-8}, doi=
{10.1109/LRA.2022.3187264}}