This repository is an experimental framework for Multi Object Tracking analysis. Here you can find multiple detectors, trackers and evaluation metrics. Also with the following documentation you might implement your own model in the framework.
The repository is divided in folders:
- tools : Useful tools for visualize the gt or outputs.
- dataset : Datasets used for Multi Object Tracking is store in this folder.
- outputs : Outputs of detectors and trackers will be save in this folder.
- detectors : Folder with detection models.
- evaluation : Contains an evaluation script with multiple metrics.
- trackers : Tracking algorithms are stored here.
In the following sections we will explore how are organized this folders and how to run experiments.
To setup the environment run bash setup.sh. It is recomended to configure the flags in order to get the best performance running the experiments. This script could take 1 hour to run.
Download datasets and create enviroments:
bash setup.sh
Run detection step:
python detectors/main.py --detector <detector_name>
To run with further configuration check detectors folder.
Run tracking step:
python trackers/main.py --detector <detector_name> --tracker <tracker_name>
To run with further configuration check trackers folder.
Evaluating results:
python evaluation/mAP/main.py
python evaluation/scripts/run_mot_challenge.py --BENCHMARK <dataset_name> --USE_PARALLEL True --NUM_PARALLEL_CORES 8 --TRACKERS_TO_EVAL <traker_name>
python evaluation/efforce/main.py
python evaluation/create_table.py
You can find detection models in the folder detectors. Each model has its own folder. If you want some tips of how implement a new detector read the README.md file in that folder.
The implemented detectors are listed below:
| Detector Name | Code | Publication Year | Publication |
|---|---|---|---|
| Faster-RCNN | https://pytorch.org/ | 2016 | https://arxiv.org/abs/1506.01497 |
| Yolo V4 | https://github.com/AlexeyAB/darknet | 2020 | https://arxiv.org/abs/2004.10934 |
| Yolo V3 | https://github.com/AlexeyAB/darknet | 2018 | https://arxiv.org/abs/1804.02767 |
| RetinaNet | https://pytorch.org/ | 2018 | https://arxiv.org/abs/1708.02002 |
| Mask R-CNN | https://pytorch.org/ | 2018 | https://arxiv.org/abs/1703.06870 |
| Keypoint R-CNN | https://pytorch.org/ | ||
| EfficientDet | https://github.com/google/automl | 2020 | https://arxiv.org/abs/1911.09070 |
Example of running detection step.
python detectors/main.py --detector <detector_name>
If needed help, run the script with -h flag.
You can find detection models in the folder trackers. Each model has its own folder. If you want some tips of how implement a new detector read the README.md file in that folder.
The implemented trackers are listed below:
| Tracker Name | Code | Publication Year | Publication | Journal | Type |
|---|---|---|---|---|---|
| Sort | https://github.com/abewley/sort | 2017 | https://arxiv.org/abs/1602.00763 | IEEE Conference | Online |
| Deep Sort | https://github.com/nwojke/deep_sort | 2017 | https://arxiv.org/abs/1703.07402 | IEEE Conference | Online |
| SST | https://github.com/shijieS/SST | 2018 | https://arxiv.org/abs/1703.07402 | Arxiv | Online |
| UMA | https://github.com/yinjunbo/UMA-MOT | 2020 | https://arxiv.org/abs/2003.11291 | CVPR | Online |
Example of running tracking step.
python trackers/main.py --detector <tracker_name>
If needed help, run the script with -h flag.
The base dataset is compose of the following datasets. This sets will be automaticly downloaded running the setup.sh script.
| Dataset Name | URL | Download |
|---|---|---|
| MOT20 | MOT Challenge | https://motchallenge.net/data/MOT20.zip |
| MOT17 | MOT Challenge | https://motchallenge.net/data/MOT17.zip |
| VisDrone | VisDrone | (Create an account) |
The folder structure used to store the data is based on MOT Challenge structure. In this repository are two folders where to store data: dataset where is loaded the raw data and outputs where are stored the outputs of the detector and tracking algorithms. The following sub-sections explore how the data is strutured in those folders.
In the dataset folder you can find the GT and the images of each set of data.
The directory contains folders for each set of data. Inside them the are folders for each sequence of video. For each sequence there is a gt/gt.txt file and a folder img1 with a list images associated to each frame. Below you can find an example of the tree structure.
dataset/
├── MOT20
│ ├── MOT20-01
│ │ ├── gt
│ │ │ └── gt.txt
│ │ ├── img1
│ │ │ └── <frame_id>.jpg
│
└── MOT17
.
.
In the output directory you can find the results of the detectors and tracker models.
outputs/
├── detections
│ ├── public
│ │ └── MOT20
│ │ └── <sequence name>
│ │ └── det
│ │ └── det.txt
│ └── <detector>
│ └── MOT20
│ └── ...
│
└── tracks
└── <tracker>
├── public
│ └── MOT20
│ └── <sequence name>.txt
└── <detector>
└── MOT20
└── ...