AIC2023_Track1_Nota

[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},
}

Terms of use

The multi-camera people tracking system published in this repository was developed by combining several modules (e.g., object detector, re-identification model, multi-object tracking model). Commercial use of any modifications, additions, or newly trained parameters made to combine these modules is not allowed. However, commercial use of the unmodified modules is allowed under their respective licenses. If you wish to use the individual modules commercially, you may refer to their original repositories and licenses provided below.

Object detector (license) link : Github, License

Re-identification model (license) link : Github, License

Multi-object tracking model (license) link : Github, License