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This project demonstrates the design and implementation of a Multi-Target Multi-Camera Tracking (MTMCT) solution.
Pipeline of our solution:
Results and comparisons with FairMOT and wda_tracker trained and tested on a 6x2-minute MTA dataset
| Method | Single-Camera | Multi-Camera | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| MOTA | IDF1 | IDs | MT | ML | MOTA | IDF1 | IDs | MT | ML | |
| WDA | 58.2 | 37.3 | 534.2 | 16.8% | 17.2 | 46.6 | 19.8 | 563.8 | 6.5% | 7.0% |
| FairMOT | 64.1 | 48.0 | 588.2 | 34.7% | 7.8% | N/A | N/A | N/A | N/A | N/A |
| Ours | 70.8 | 47.8 | 470.2 | 40.5% | 5.6% | 65.6 | 31.5 | 494.5 | 31.2% | 1.1% |
Demo on Multi Camera Track Auto (MTA) dataset
Demo GIFs can be seen here
Full-length demo videos can be found at: https://youtu.be/lS9YvbrhOdo
conda create -n mtmct python=3.7.7 -y
conda activate mtmct
pip install -r requirements.txt
Install dependencies for FairMOT:
cd trackers/fair
conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch
pip install cython
pip install -r requirements.txt
cd DCNv2
./make.sh
conda install -c conda-forge ffmpeg
Go to https://github.com/schuar-iosb/mta-dataset to download the MTA data. Or use other datasets that match the same format.
Modify config files under tracker_configs and clustering_configs for customization. Create a work_dirs and see more instructions at FairMOT and wda_tracker.
E.g. in configs/tracker_configs/fair_high_30e set the data -> source -> base_folder to your dataset location.
Run single and the multi-camera tracking with one script:
sh start.sh fair_high_30e
Modify config files under tracker_configs and clustering_configs for customization. More instructions can be found at FairMOT and wda_tracker.
A large part of the code is borrowed from FairMOT and wda_tracker. The dataset used is MTA
Ruizhe Zhang is the author of this repository and the corresponding report, the copyright belongs to Wireless System Research Group (WiSeR), McMaster University.