AICup_MCMOT_Baseline

Warning

This baseline only provides single-camera object tracking and does not include a ReID model for cross-camera association and tracking.

Our baseline is base on Ultralytics YOLOv8 framework, providing multiple object tracking (MOT) algorithms: BoT-SORT and Bytetrack.
In the BoT-SORT architecture, the ReID model used needs to be implemented and replaced independently.
For more details, you can refer to the following Repository:
- Ultralytics YOLOv8
- Offical BoT-SORT Implementation

Installation

Step1. Fork this Repository

Step2. Clone your Repository to your device

Step3. Create Conda environment and install pytorch >= 2.0

conda create -n AI_CUP python=3.9
conda activate AI_CUP

Step4. Install other requirement

cd AICup_MCMOT_Baseline
pip install -r requirements.txt

Data Preparation

Download the AI_CUP dataset, 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
│   │   ├── ...

run the AICUP_to_YOLOv8.py by following commend:

cd AICup_MCMOT_Baseline
python utils/AICUP_to_YOLOv8.py --AICUP_dir "your AICUP dataset path" --YOLOv8_dir "converted dataset directory" --train_ratio "The ratio of the train set when splitting the train set and the validation set (default 0.8)"

The file tree after conversion by AICUP_to_YOLOv8.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
    │   │   ├── ...
    ├── MCMOT.yaml (Yolo config)

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

The Ground Truth after conversion by AICUP_to_YOLOv8.py is as follows:

Warning

The coordinates and dimensions of the Ground Truth data are normalized

class	center_x	center_y	width	height
0	0.704687	0.367592	0.032291	0.1

# image_name1.txt

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

Training Yolov8

You can see the how to used the training, valid, and predict function at

cd AICup_MCMOT_Baseline
python train_YOLO/train_YOLOv8.py
python train_YOLO/val_YOLOv8.py

The Result will be save at runs/detect folder

Tracking and create the submit file

You Can run our sample code: detect.py by following commend:

cd AICup_MCMOT_Baseline
python detect.py --data_path "your dataset path" --yolo_path "your model weight path" --devices "0, 1"

Config:

data_path: Your dataset folder
yolo_path: Your YOLO model's weight
out_file_path: Where your output file save
devices: 'cpu' or 'gpu number', support multi-GPU training
video_shape: original video resolution
imgsz: yolo input size
save_res: bool, save submit file, default = True

The submission result default save at runs/tracking_res

Where to add ReID model in Tracking algorithm

if you want to add ReID model in YOLOv8's tracking, you need to edit YOLOv8 source code at: ultralytics/ultralytics/trackers/bot_sort.py

For example, In the conda environment, the source code is locate at:

.conda/envs/(your_env_name)/lib/python3.9/site-packages/ultralytics/trackers/bot_sort.py

Evulate Format

Using same format on py-motmetrics

Warning

The evulate images resolution is 1280 * 720

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

How to Evulate your submission

Before evaluate, you need to run utils/AICUP_to_MOT15.py to convert ground truth into submission format:

cd AICup_MCMOT_Baseline
python utils/AICUP_to_MOT15.py --AICUP_dir "your AICUP dataset path" --MOT15_dir "converted dataset directory" --imgsz "img size, (height, width)"

You can used utils/evaluate.py to Evulate your submission by following commend:

cd AICup_MCMOT_Baseline
python utils/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 tree is as follows:

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

Than you can get the result:

Result

ToDo

Fix detect.py output
Implement AICUP_to_MOT15.py
Upload baseline score
Release test set

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
demo_pic		demo_pic
train_YOLO		train_YOLO
utils		utils
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
detect.py		detect.py
requirements.txt		requirements.txt

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Repository files navigation

AICup_MCMOT_Baseline

Installation

Data Preparation

Ground Truth Format

Training Yolov8

Tracking and create the submit file

Config:

Where to add ReID model in Tracking algorithm

Evulate Format

How to Evulate your submission

Result

ToDo

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Stanley5249/AICup_MCMOT_Baseline

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AICup_MCMOT_Baseline

Installation

Data Preparation

Ground Truth Format

Training Yolov8

Tracking and create the submit file

Config:

Where to add ReID model in Tracking algorithm

Evulate Format

How to Evulate your submission

Result

ToDo

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