hejiawei2023/UG3DMOT

UG3DMOT

Abstract

In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage. Namely, the inherent uncertainties existing in tracks and detections are overlooked. In this work, we discard the commonly used deterministic tracks and deterministic detections for data association, instead, we propose to model tracks and detections as random vectors in which uncertainties are taken into account. Then, based on the Jensen-Shannon divergence, the similarity between two multidimensional distributions, i.e. track and detection, is evaluated for data association purposes. Lastly, the level of track uncertainty is incorporated in our cost function design to guide the data association process. We achieve state-of-the-art results across several MOT tasks on the KITTI and NuScenes datasets.

Benchmark result

We participated in the benchmark challenge of KITTI and NuScenes, you can check our real-time ranking through the following two links

Prepare data

Kitti Dataset

# For Kitti Dataset    
└── kitti_tracking
       ├── testing 
       |      ├──calib
       |      |    ├──0000.txt
       |      |    ├──....txt
       |      |    └──0028.txt
       |      ├──image_02
       |      |    ├──0000
       |      |    ├──....
       |      |    └──0028
       |      ├──pose
       |      |    ├──0000
       |      |    |    └──pose.txt
       |      |    ├──....
       |      |    └──0028
       |      |         └──pose.txt
       |      ├──label_02
       |      |    ├──0000.txt
       |      |    ├──....txt
       |      |    └──0028.txt
       |      └──velodyne
       |           ├──0000
       |           ├──....
       |           └──0028      
       └── training # the structure is same as testing set
              ├──calib
              ├──image_02
              ├──pose
              ├──label_02
              └──velodyne 

# Detections
└── casa
       ├── training
       |      ├──0000
       |      |    ├──000001.txt
       |      |    ├──....txt
       |      |    └──000153.txt
       |      ├──...
       |      └──0020
       └──testing 

Notes

If you are using Casa detector, then you don't need to modify the measurement covariance in cov_data. If you use other detectors, you need to re-estimate its uncertainty using the method from here

NuScenes Dataset

Download nuScenes perception dataset

# For nuScenes Dataset         
└── NUSCENES_DATASET_ROOT
       ├── samples       
       ├── sweeps       
       ├── maps         
       ├── v1.0-trainval 
       ├── v1.0-test

To extract timestamp infos/ego infos, run the following:

bash preparedata/nuscenes/nu_preparedata.sh <path to nuScenes>/nuscenes

Run the following to convert detection results into to .npz files. The detection results should be in official nuScenes submission format(.json)

bash preparedata/nuscenes/nu_convert_detection.sh  <path to detection results>/detection_result.json <detector name>

Notes

We use the Centerpoint detector. If you use other detectors, you need to re-estimate its uncertainty using the method from here and modify the measurement covariance in cov_data

Installation

1. Ubuntu 20.04 and python 3.6
2. Clone the github repository git clone https://github.com/hejiawei2023/UG3DMOT.git
3. Create and activate a conda environment
4. Installation package pip install -r requirement

Quick start

• Please modify the dataset path, detections path and result path in the Kitti yaml file or NuScenes yaml file to your own path.
• Then run python3 ./main_kitti.py or python3 ./main_nuscenes.py
• If you are using the NuScenes dataset, you also need to run bash evaluation/nuscenes/pipeline.sh <trakcer name> to merge the results into .json format

Acknowledgement

The processing code for the KITTI dataset comes from here

The processing code for the NuScenes dataset comes from here

Many thanks to their wonderful work!