AICUP Baseline: BoT-SORT

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky

Important

This baseline is based on the code released by the original author of BoT-SORT. Special thanks for their release.

Warning

This baseline only provides single-camera object tracking and does not include cross-camera association.
Due to our dataset's low frame rate (fps: 1), we have disabled the Kalman filter in BoT-SORT. Low frame rates can cause the Kalman filter to deviate, hence we only used appearance features for tracking in this baseline.

ToDo

Complete evaluation guide
Visualize results on AICUP train_set
Release test set

Visualization results on AICUP train_set

Installation

The code was tested on Ubuntu 20.04 & 22.04

BoT-SORT code is based on ByteTrack and FastReID.
Visit their installation guides for more setup options.

Setup with Conda

Step 1. Create Conda environment and install pytorch.

conda create -n botsort python=3.7
conda activate botsort

Step 2. Install torch and matched torchvision from pytorch.org.
The code was tested using torch 1.11.0+cu113 and torchvision==0.12.0

Step 3. Fork this Repository and clone your Repository to your device

Step 4. Install numpy first!!

pip install numpy

Step 5. Install requirement.txt

pip install -r requirement.txt

Step 6. Install pycocotools.

pip install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

Step 7. Others

# Cython-bbox
pip install cython_bbox

# faiss cpu / gpu
pip install faiss-cpu
pip install faiss-gpu

Data Preparation

Download the AI_CUP dataset, the original dataset structure is:

├── train
│   ├── images
│   │   ├── 0902_150000_151900 (Timestamp: Date_StartTime_EndTime)
│   │   │  ├── 0_00001.jpg (CamID_FrameNum)
│   │   │  ├── 0_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 1_00001.jpg (CamID_FrameNum)
│   │   │  ├── 1_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 7_00001.jpg (CamID_FrameNum)
│   │   │  ├── 7_00002.jpg
│   │   ├── 0902_190000_191900 (Timestamp: Date_StartTime_EndTime)
│   │   │  ├── 0_00001.jpg (CamID_FrameNum)
│   │   │  ├── 0_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 1_00001.jpg (CamID_FrameNum)
│   │   │  ├── 1_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 7_00001.jpg (CamID_FrameNum)
│   │   │  ├── 7_00002.jpg
│   │   ├── ...
│   └── labels
│   │   ├── 0902_150000_151900 (Timestamp: Date_StartTime_EndTime)
│   │   │  ├── 0_00001.txt (CamID_FrameNum)
│   │   │  ├── 0_00002.txt
│   │   │  ├── ...
│   │   │  ├── 1_00001.txt (CamID_FrameNum)
│   │   │  ├── 1_00002.txt
│   │   │  ├── ...
│   │   │  ├── 7_00001.txt (CamID_FrameNum)
│   │   │  ├── 7_00002.txt
│   │   ├── 0902_190000_191900 (Timestamp: Date_StartTime_EndTime)
│   │   │  ├── 0_00001.txt (CamID_FrameNum)
│   │   │  ├── 0_00002.txt
│   │   │  ├── ...
│   │   │  ├── 1_00001.txt (CamID_FrameNum)
│   │   │  ├── 1_00002.txt
│   │   │  ├── ...
│   │   │  ├── 7_00001.txt (CamID_FrameNum)
│   │   │  ├── 7_00002.txt
│   │   ├── ...
--------------------------------------------------
├── test
│   ├── images
│   │   ├── 0902_150000_151900 (Timestamp: Date_StartTime_EndTime)
│   │   │  ├── 0_00001.jpg (CamID_FrameNum)
│   │   │  ├── 0_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 1_00001.jpg (CamID_FrameNum)
│   │   │  ├── 1_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 7_00001.jpg (CamID_FrameNum)
│   │   │  ├── 7_00002.jpg
│   │   ├── 0902_190000_191900 (Timestamp: Date_StartTime_EndTime)
│   │   │  ├── 0_00001.jpg (CamID_FrameNum)
│   │   │  ├── 0_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 1_00001.jpg (CamID_FrameNum)
│   │   │  ├── 1_00002.jpg
│   │   │  ├── ...
│   │   │  ├── 7_00001.jpg (CamID_FrameNum)
│   │   │  ├── 7_00002.jpg
│   │   ├── ...

Ground Truth Format

Each image corresponds to a text file, an example is provided below:

Warning

The coordinates and dimensions of the Ground Truth data are normalized

class	center_x	center_y	width	height	track_ID
0	0.704687	0.367592	0.032291	0.1	1

# image_name1.txt

0 0.704687 0.367592 0.032291 0.1 1
0 0.704166 0.403703 0.030208 0.087037 2
0 0.929166 0.710185 0.051041 0.162962 3
0 0.934114 0.750925 0.084895 0.162962 4
0 0.780208 0.273148 0.023958 0.062962 5
0 0.780989 0.246296 0.022395 0.066666 6

Prepare ReID Dataset

For training the ReID, detection patches must be generated as follows:

cd <BoT-SORT_dir>

# For AICUP 
python fast_reid/datasets/generate_AICUP_patches.py --data_path <dataets_dir>/AI_CUP_MCMOT_dataset/train

Tip

You can link dataset to FastReID export FASTREID_DATASETS=<BoT-SORT_dir>/fast_reid/datasets. If left unset, the default is fast_reid/datasets

Prepare YOLOv7 Dataset

Warning

We only implemented the fine-tuning interface for yolov7 If you need to change the object detection model, please do it yourself.

run the yolov7/tools/AICUP_to_YOLOv7.py by the following command:

cd <BoT-SORT_dir>
python yolov7/tools/AICUP_to_YOLOv7.py --AICUP_dir datasets/AI_CUP_MCMOT_dataset/train --YOLOv7_dir datasets/AI_CUP_MCMOT_dataset/yolo

The file tree after conversion by AICUP_to_YOLOv7.py is as follows:

/datasets/AI_CUP_MCMOT_dataset/yolo
    ├── train
    │   ├── images
    │   │   ├── 0902_150000_151900_0_00001.jpg (Date_StartTime_EndTime_CamID_FrameNum)
    │   │   ├── 0902_150000_151900_0_00002.jpg
    │   │   ├── ...
    │   │   ├── 0902_150000_151900_7_00001.jpg
    │   │   ├── 0902_150000_151900_7_00002.jpg
    │   │   ├── ...
    │   └── labels
    │   │   ├── 0902_150000_151900_0_00001.txt (Date_StartTime_EndTime_CamID_FrameNum)
    │   │   ├── 0902_150000_151900_0_00002.txt
    │   │   ├── ...
    │   │   ├── 0902_150000_151900_7_00001.txt
    │   │   ├── 0902_150000_151900_7_00002.txt
    │   │   ├── ...
    ├── valid
    │   ├── images
    │   │   ├── 1015_190000_191900_0_00001.jpg (Date_StartTime_EndTime_CamID_FrameNum)
    │   │   ├── 1015_190000_191900_0_00002.jpg
    │   │   ├── ...
    │   │   ├── 1015_190000_191900_7_00001.jpg
    │   │   ├── 1015_190000_191900_7_00002.jpg
    │   │   ├── ...
    │   └── labels
    │   │   ├── 1015_190000_191900_0_00001.txt (Date_StartTime_EndTime_CamID_FrameNum)
    │   │   ├── 1015_190000_191900_0_00002.txt
    │   │   ├── ...
    │   │   ├── 1015_190000_191900_7_00001.txt
    │   │   ├── 1015_190000_191900_7_00002.txt
    │   │   ├── ...

Model Zoo for MOT17 & COCO

Tip

We recommend using YOLOv7 as the object detection model for tracking

Download and store the trained models in 'pretrained' folder as follows:

<BoT-SORT_dir>/pretrained

We used the publicly available ByteTrack model zoo trained on MOT17, MOT20 and ablation study for YOLOX object detection.
Author's trained ReID models can be downloaded from MOT17-SBS-S50, MOT20-SBS-S50.
For multi-class MOT use YOLOX or YOLOv7 trained on COCO (or any custom weights).

Training (Fine-tuning)

Train the ReID Module for AICUP

After generating the AICUP ReID dataset as described in the 'Data Preparation' section.

cd <BoT-SORT_dir>

# For training AICUP 
python3 fast_reid/tools/train_net.py --config-file fast_reid/configs/AICUP/bagtricks_R50-ibn.yml MODEL.DEVICE "cuda:0"

The training results are stored by default in logs/AICUP/bagtricks_R50-ibn. The storage location and model hyperparameters can be modified in fast_reid/configs/AICUP/bagtricks_R50-ibn.yml.

You can refer to fast_reid/fastreid/config/defaults.py to find out which hyperparameters can be modified.

Refer to FastReID repository for additional explanations and options.

Important

Since we did not generate the query and gallery datasets required for evaluation when producing the ReID dataset (MOT17_ReID provided by BoT-SORT also not provide them), please skip the following TrackBack when encountered after training completion.

Traceback (most recent call last):
...
File "./fast_reid/fastreid/evaluation/reid_evaluation.py", line 107, in evaluate
    cmc, all_AP, all_INP = evaluate_rank(dist, query_pids, gallery_pids, query_camids, gallery_camids)
  File "./fast_reid/fastreid/evaluation/rank.py", line 198, in evaluate_rank
    return evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03)
  File "rank_cy.pyx", line 20, in rank_cy.evaluate_cy
  File "rank_cy.pyx", line 28, in rank_cy.evaluate_cy
  File "rank_cy.pyx", line 240, in rank_cy.eval_market1501_cy
AssertionError: Error: all query identities do not appear in gallery

Fine-tune YOLOv7 for AICUP

The dataset path is configured in yolov7/data/AICUP.yaml.
The model architecture can be configured in yolov7/cfg/training/yolov7-AICUP.yaml.
Training hyperparameters are configured in yolov7/data/hyp.scratch.custom.yaml (default is yolov7/data/hyp.scratch.p5.yaml).

Single GPU finetuning for AICUP dataset

yolov7_training.pt yolov7x_training.pt yolov7-w6_training.pt yolov7-e6_training.pt yolov7-d6_training.pt yolov7-e6e_training.pt

cd <BoT-SORT_dir>
# finetune p5 models
python yolov7/train.py --device 0 --batch-size 16 --epochs 50 --data yolov7/data/AICUP.yaml --img 1280 1280 --cfg yolov7/cfg/training/yolov7-AICUP.yaml --weights 'pretrained/yolov7-e6e.pt' --name yolov7-AICUP --hyp data/hyp.scratch.custom.yaml

# finetune p6 models
python yolov7/train_aux.py --device 0 --batch-size 16 --epochs 50 --data yolov7/data/AICUP.yaml --img 1280 1280 --cfg yolov7/cfg/training/yolov7-w6-AICUP.yaml --weights 'pretrained/yolov7-e6e.pt' --name yolov7-w6-AICUP --hyp data/hyp.scratch.custom.yaml

For multiple GPU training and other details, please refer to YOLOv7-Training.

The training results will be saved by default at runs/train.

Tracking and creating the submission file for AICUP (Demo)

Warning

We only implemented the tools/mc_demo_yolov7.py( mc mean multi-class) for AICUP
If you need to use other tracking programs in tools, please do it yourself.

Track one <timestamp> with BoT-SORT(-ReID) based YOLOv7 and multi-class (We only output class: 'car').

cd <BoT-SORT_dir>
python3 tools/mc_demo_yolov7.py --weights pretrained/yolov7-e6e.pt --source AI_CUP_MCMOT_dataset/train/images/<timestamp> --device "0" --name "<timestamp>" --fuse-score --agnostic-nms --with-reid --fast-reid-config fast_reid/configs/AICUP/bagtricks_R50-ibn.yml --fast-reid-weights logs/AICUP/bagtricks_R50-ibn/model_00xx.pth

If you want to track all <timestamps> in the directory, you can execute the bash file we provided.

cd <BoT-SORT_dir>
bash tools/track_all_timestamps.sh --weights "pretrained/yolov7-e6e.pt" --source-dir "AI_CUP_MCMOT_dataset/train/images" --device "0" --fast-reid-config "fast_reid/configs/AICUP/bagtricks_R50-ibn.yml" --fast-reid-weights "logs/AICUP/bagtricks_R50-ibn/model_00xx.pth"

The submission file and visualized images will be saved by default at runs/detect/<timestamp>.

Evaluation format

The evaluation format is the same as py-motmetrics.

Caution

The images resolution for evaluation is 1280 * 720

frame_id	track_id	bb_left	bb_top	bb_width	bb_height	conf	3d_x	3d_y	3d_z
1	1	843	742	30	30	0.8	-1	-1	-1

Evaluate your submission

Before evaluation, you need to run tools/datasets/AICUP_to_MOT15.py to convert ground truth into submission format:

cd <BoT-SORT_dir>
python tools/datasets/AICUP_to_MOT15.py --AICUP_dir "your AICUP dataset path" --MOT15_dir "converted dataset directory" --imgsz "img size, (height, width)"

You can use tools/evaluate.py to evaluate your submission by the following command:

cd <BoT-SORT_dir>
python tools/evaluate.py --gt_dir "Path to the ground truth directory" --ts_dir "Path to the tracking result directory"

The gt_dir and ts_dir file trees are as follows:

├── gt_dir
│   ├── 0902_150000_151900.txt
│   ├── 0902_190000_191900.txt
│   ├── ...
├── ts_dir
│   ├── 0902_150000_151900.txt
│   ├── 0902_190000_191900.txt
│   ├── ...

Then you can get the result:

Note

Our camera motion compensation module is based on the OpenCV contrib C++ version of VideoStab Global Motion Estimation, which currently does not have a Python version.
Motion files can be generated using the C++ project called 'VideoCameraCorrection' in the GMC folder.
The generated files can be used from the tracker.

In addition, python-based motion estimation techniques are available and can be chosen by passing
'--cmc-method' <files | orb | ecc> to demo.py or track.py.

Citation

@article{aharon2022bot,
  title={BoT-SORT: Robust Associations Multi-Pedestrian Tracking},
  author={Aharon, Nir and Orfaig, Roy and Bobrovsky, Ben-Zion},
  journal={arXiv preprint arXiv:2206.14651},
  year={2022}
}

Acknowledgement

A large part of the codes, ideas and results are borrowed from

Thanks for their excellent work!

aquastripe / aicup-botsort Goto Github PK

aicup-botsort's Introduction

AICUP Baseline: BoT-SORT

ToDo

Visualization results on AICUP train_set

Installation

Setup with Conda

Data Preparation

Ground Truth Format

Prepare ReID Dataset

Prepare YOLOv7 Dataset

Model Zoo for MOT17 & COCO

Training (Fine-tuning)

Train the ReID Module for AICUP

Fine-tune YOLOv7 for AICUP

Tracking and creating the submission file for AICUP (Demo)

Evaluation format

Evaluate your submission

Note

Citation

Acknowledgement

aicup-botsort's People

Contributors

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