Skip to content

MCCA-MOT: Multimodal Collaboration-Guided Cascade Association Network for 3D Multi-Object Tracking

Notifications You must be signed in to change notification settings

yuanfuture/MCCA-MOT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

58 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation


framework

Abstract

In consideration of the duplicate rate requirement at the time of submission of the article, the abstract has been removed and will be added again later.

Perfoemance

KITTI test set Car KITTI test set Pedestrian

Dependencies

python: 3.8.16

pytorch: 1.9.1

Torchvision: 0.10.1

CUDA: 11.1

CUDNN: 8.0.5

Started

1. Clone the github repository.

git clone https://github.com/yuanfuture/MCCA-MOT

2. Dataset preparation

Please go to KITTI official to download the required datasets. object tracking dataset.

The final dataset organization should be like this:

```
MCCA-MOT
├── data
│   ├── kitti
│   │   │── training
│   │   │   ├──calib & velodyne & label_02 & image_02 & depth_2 & (optional: planes) 
│   │   │── testing
│   │   │   ├──calib & velodyne & image_02 
```

3. Install dependency

cd your_path/MCCA-MOT
pip install -r requirements.txt

4. Run

python main.py

5. KITTI MOT Evaluation

If you want to evaluate the tracking results using the evaluation tool on the KITTI website, you will need to go https://github.com/JonathonLuiten/TrackEval to download the evaluation code and follow the appropriate steps to set.

the following results will be obtained.

HOAT( ↑) AssA( ↑) LocA(↑) MOTA(↑) MOTP(↑) FP(↓) FN(↓) IDSW(↓)
Car 79.31% 83.49% 88.60% 86.71% 87.51% 3992 513 66
Pedestrian 51.79% 56.95% 78.52% 60.36% 74.50% 7687 1317 173

Acknowledgement

Refer to CADDN for image feature promotion, and refer to SFD and TWISE for obtaining pseudo point cloud data. Many thanks to their wonderful work!

Citation

If you find this work useful, please consider to cite our paper:

@ARTICLE{  
author={Hengyuan Liu},  
journal={},   
title={MCCA-MOT: Multimodal Collaboration-Guided Cascade Association Network for 3D Multi-Object Tracking},   
year={2024},  volume={},  number={},  pages={},  doi={}

About

MCCA-MOT: Multimodal Collaboration-Guided Cascade Association Network for 3D Multi-Object Tracking

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages