https://github.com/hailanyi/3D-Multi-Object-Tracker
A project for 3D multi-object tracking
https://github.com/hailanyi/3D-Multi-Object-Tracker
3d-object-tracking kitti multi-object-tracking
Last synced: 3 months ago
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A project for 3D multi-object tracking
- Host: GitHub
- URL: https://github.com/hailanyi/3D-Multi-Object-Tracker
- Owner: hailanyi
- License: apache-2.0
- Created: 2021-06-06T05:40:23.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2023-06-15T05:36:00.000Z (about 2 years ago)
- Last Synced: 2024-10-28T06:57:42.444Z (8 months ago)
- Topics: 3d-object-tracking, kitti, multi-object-tracking
- Language: Python
- Homepage:
- Size: 24.6 MB
- Stars: 316
- Watchers: 5
- Forks: 53
- Open Issues: 12
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# 3D Multi-Object Tracker
This project is developed for tracking multiple objects in 3D scene. The visualization code is from
[here](https://github.com/hailanyi/3D-Detection-Tracking-Viewer). This is a re-produced and simplified version of [paper](https://ieeexplore.ieee.org/abstract/document/9352500). We have made some changes to support more detectors and adapt the HOTA metric.
We also provided a simple [guide](https://github.com/hailanyi/3D-Multi-Object-Tracker/blob/master/doc/submit.md) to submit results to the KITTI test set.
## Features
* Fast: currently, the codes can achieve 700 FPS using only CPU (not include detection and data op), can perform tracking
on all kitti val sequence in several seconds.
* Support online, near online and global implementation.
The overall framework of design is shown below:
## Kitti Results
**Car/Pedestrian** tracking results on the Kitti tracking val seq [1,6,8,10,12,13,14,15,16,18,19]
using second-iou, point-rcnn and pv-rcnn detections. We also followed the **HOTA** metric, and tuned the parameters by first
considering the **HOTA** performance.
|detector|online|near online (latency=2s)|global|
|:---:|:---:|:---:|:---:|
|point-rcnn|76.653|77.576|78.853|
|second-iou|77.29/48.011|78.17/50.362|78.799/51.20|
|pv-rcnn|78.289/49.662|79.48/50.824|80.075/51.753|
Online: CA-based KF + greedy matching.
Near online: online + rescoring tracks in a temporal window.
Global: online + rescoring tracks globally.
* 2022/9/23 Update CasTrack. The detections are obtained by [CasA detector](https://github.com/hailanyi/CasA) trained on KITTI 3D detection trainval set. Currently **rank first** on the KITTI tracking dataset :fire:!
|Detector|online|near online (latency=2s)|global|detections (training & testing set)|
|:---:|:---:|:---:|:---:|:---:|
|[CasA](https://github.com/hailanyi/CasA)|82.59|83.24|83.34| [detections download link(40M)](https://drive.google.com/file/d/1LaousWNTldOV1IhdcGDRM_UGi5BFWDoN/view?usp=sharing)
* Update VirConvTrack (2023/3/7), the detections are obtained by [VirConv-T detector](https://github.com/hailanyi/VirConv) trained on KITTI 3D detection trainval set.
|Detector|online|near online (latency=2s)|global|detections (training & testing set)|
|:---:|:---:|:---:|:---:|:---:|
|[VirConv](https://github.com/hailanyi/VirConv)|85.40|85.98|86.17| [detections download link(8M)](https://drive.google.com/file/d/1i-SM3RWYLzdXTGRnhjTI7vwkk2CHbh7b/view?usp=sharing)
## Waymo Results
**Vehicle/Pedestrian/Cyclist** online tracking results, evaluated by **MOTA/L2** metrics of Waymo benchmark.
* 2022/9/23 Update [CasTrack-waymo](https://github.com/hailanyi/CasTrack-waymo), currently **ranks third** on Waymo tracking benchmark :fire:! You can download the CasA detections from [here](https://drive.google.com/file/d/1xDTXrKf3azvW0m6e3xMV1PluICRDstPk/view?usp=sharing)
(Please register with waymo before downloading, as the Waymo open dataset is under strict non-commercial license).
|detector|set|Vehicle_L2|Pedestrian_L2|Cyclist_L2| ALL_NS_L2 |
|:---:|:---:|:---:|:---:|:---:|:---:|
|CasA|val set|59.30|62.78|61.72|61.27|
|CasA|test set|63.66|64.79|59.34|62.60|
## Prepare data
You can download the Kitti tracking pose data from [here](https://drive.google.com/drive/folders/1Vw_Mlfy_fJY6u0JiCD-RMb6_m37QAXPQ?usp=sharing), and
you can download the point-rcnn, second-iou and pv-rcnn detections from [here](https://drive.google.com/file/d/1zVWFGwRqF_CBP4DFJJa4nBcu-z6kpF1R/view?usp=sharing).
You can download the CasA detections(including training & testing set) from [here](https://drive.google.com/file/d/1LaousWNTldOV1IhdcGDRM_UGi5BFWDoN/view?usp=sharing).
To run this code, you should organize Kitti tracking dataset as below:
```
# Kitti Tracking 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
```
└── point-rcnn
├── training
| ├──0000
| | ├──000001.txt
| | ├──....txt
| | └──000153.txt
| ├──...
| └──0020
└──testing
```
## Requirements
```
python3
numpy
opencv
yaml
```
## Quick start
* Please modify the dataset path and detections path in the [yaml file](./config/online/pvrcnn_mot.yaml)
to your own path.
* Then run ``` python3 kitti_3DMOT.py config/online/pvrcnn_mot.yaml```
* The results are automatically saved to ```evaluation/results/sha_key/data```, and
evaluated by HOTA metrics.
## Notes
The evaluation codes are copied from [Kitti](https://github.com/JonathonLuiten/TrackEval).