[CVPRW] 2023 AI City Challenge: Multi-camera People Tracking With Mixture of Realistic and Synthetic Knowledge

The 2nd Place Submission to The 7th NVIDIA AI City Challenge (2023) Track 1: Multi-camera people tracking

Framework

Setup

Install environment

conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge

pip install -r requirements.txt

pip install -e .

Setup environment path:

To begin, rename the file .env.list to .env.

Then, in the .env, update the following variables:

DATASETS.ROOT_DIR: the path to the dataset directory
PRETRAIN_ROOT: the path to the pretrain directory

Example:

DATASETS.ROOT_DIR='/mnt/ssd8tb/quang/AIC23_Track1_MTMC_Tracking/'
PRETRAIN_ROOT='/mnt/ssd8tb/quang/pretrain'

Go to scripts/tracking.sh and change the DATASET_DIR path on line 2 to the correct path for your dataset.

Extract frames

python tools/extract_frame.py

Directory structure:

Code structure

Multi-camera-People-Tracking-With-Mixture-of-Realistic/
├── assets/
├── configs/
├── datasets/
│   ├── detection/
│   │   └── Yolo/
│   ├── reid/
│   ├── pretrain/
│       ├── HRNet_W48_C_ssld_pretrained.pth
│       ├── -------
│       └── jx_vit_base_p16_224-80ecf9dd.pth
│   └── ROI/
├── output/
│   └── weight/
│       ├── HrNetW48/
│           └── HrNet_epoch_3.ckpt
│       ├── transformer/
│           └── transformer_epoch_4.ckpt
│       └── trans_local/
│           └── trans_local_epoch_3.ckpt
├── outputs/
├── -------
├── scripts/
├── src/
├── -------
└── tools/

Data structure:

Please download the reid train from the link above. Then put it under the path: AIC23_Track1_MTMC_Tracking/

AIC23_Track1_MTMC_Tracking/
├── test/
├── train/
├── validation/
├── person_reid/
│   ├── gallery/
│   ├── query/
│   └── train/
└──

Inference

For fast inference, please visit this link and download all trained models and datasets.

To save time on detection, please use the pre-detected dataset.

Tracking

Extract feature for tracking

bash scripts/feature_extract_tracking.sh

Tracking:

bash scripts/tracking.sh

Single-camera matching and multi-camera matching

Extract feature for matching:

bash scripts/feature_extract_matching.sh

Note that only scene 001 requires extracting features again. For another scene, features generated from tracking are available in the folder src/SCMT/tmp.

Matching:

bash scripts/matching.sh

Final submit

python src/submit.py

The result at outputs/track1.txt

For test ID switch

python src/matching/tracklet_id_switch.py

Training

Detection:

Please follow the detection in the Detection folder.

Reid:

bash scripts/reid_train.sh

After training, the weight will be stored in the lightning_logs/ folder. Navigate to this folder and copy the corresponding epoch weight of each model to the corresponding folder in output/weight.

The following epoch should be used for each model:

Transformer: Epoch 4
Transformer-Local: Epoch 3
HrNetW48: Epoch 3

Contact

If you have any questions, please leave an issue or contact us at nguyenquivinhquang@gmail.com.

Acknowledgement

We would like to thank the Box-Grained Reranking Matching for Multi-Camera Multi-Target Tracking repository for their outstanding tracking.

hoangph3/Multi-camera-People-Tracking-With-Mixture-of-Realistic-and-Synthetic-Knowledge