nota-github / AIC2023_Track1_Nota

Repository for 2023 AI City Challenge (Track1: Multi-Camera People Tracking)

[CVPRW2023] Addressing the Occlusion Problem in Multi-Camera People Tracking with Human Pose Estimation

The official resitory for 7th NVIDIA AI City Challenge (Track1: Multi-Camera People Tracking) from team Netspresso (Nota Inc.)

Environment

• option 1: Install dependencies in your environment

bash ./setup.sh

• option 2: Use our docker image

docker build -t aic2023/track1_nota:latest -f ./Dockerfile .
docker run -it --gpus all -v /path/to/AIC2023_Track1_Nota:/workspace/AIC2023_Track1_Nota aic2023/track1_nota:latest /bin/bash

Data & Model Preparation

1. Download the dataset and extract frames

# extract frames
python3 tools/extract_frames.py --path /path/to/AIC23_Track1_MTMC_Tracking/

2. Download the pre-trained models (Google Drive)

Make sure the data structure is like:

├── AIC2023_Track1_Nota
    └── datasets
    |   ├── S001
    |   |   ├── c001
    |   |   |   ├── frame1.jpg
    |   |   |   └── ...
    |   |   ├── ...
    |   |   └── map.png
    |   ├── ...
    |   └── S022
    |
    └── pretrained
        ├── market_mgn_R50-ibn.pth
        ├── duke_sbs_R101-ibn.pth
        ├── msmt_agw_S50.pth
        ├── market_aic_bot_R50.pth
        ├── yolov8x6.pth
        ├── yolov8x6_aic.pth
        └── yolov8x_aic.pth

Reproduce MCPT Results

Run bash ./run_mcpt.sh

The result files will be saved as follows:

├── AIC2023_Track1_Nota
    └── results
        ├── S001.txt
        ├── ...
        └── track1_submission.txt

Citation

@InProceedings{Kim_2023_CVPR,
    author    = {Jeongho Kim, Wooksu Shin, Hancheol Park and Jongwon Baek},
    title     = {Addressing the Occlusion Problem in Multi-Camera People Tracking with Human Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
}