[This repo is formed from MoT tracking iumplementation of DeepSORT () xuarehere/yolo_series_deepsort_pytorch) that can be used with "ANY" yolo-family detector.
This repo extends the basic MoT implemntation to add the count of top-ranked objects visible in scene and if required, display and saves tracks of objects in CSV.]

Table of contents

• Table of contents
• Deep Sort with PyTorch(yolo-all)
  • Introduction
  • Model
    • Object detection
    • ReID
  • Project structure
  • Dependencies
  • Quick Start
  • Training the Object model
  • Training the RE-ID model
  • Demo videos and images
• References

Deep Sort with PyTorch(yolo-all)

Introduction

This is an implement of MOT tracking algorithm deep sort. This project originates from deep_sort_pytorch. On the above projects, this project add the existing yolo detection model algorithm (YOLOv3, YOLOV4, YOLOV4Scaled, YOLOV5, YOLOV6, YOLOV7, YOLOX, YOLOR, PPYOLOE).

Model

Object detection

• MMDet
• YOLOv3
• YOLOV4
• YOLOV4Scaled
• YOLOV5
• YOLOV6
• YOLOV7
• YOLOX
• YOLOR
• PPYOLOE

ReID

• deepsort-reid
• fast-reid

Project structure

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yolovx_deepsort_pytorch/
├── 001.avi
├── checkpoint
├── configs
│   ├── deep_sort.yaml
│   ├── fastreid.yaml
│   ├── mmdet.yaml
│   ├── ppyoloe.yaml
│   ├── yolor.yaml
│   ├── yolov3_tiny.yaml
│   ├── yolov3.yaml
│   ├── yolov4Scaled.yaml
│   ├── yolov4.yaml
│   ├── yolov5.yaml
│   ├── yolov6.yaml
│   ├── yolov7.yaml
│   └── yolox.yaml
├── deep_sort
│   ├── deep
│   ├── deep_sort.py
│   ├── __init__.py
│   ├── __pycache__
│   ├── README.md
│   └── sort
├── deepsort.py
├── demo
│   ├── 1.jpg
│   ├── 2.jpg
│   └── demo.gif
├── detector
│   ├── __init__.py
│   ├── MMDet
│   ├── PPYOLOE
│   ├── __pycache__
│   ├── YOLOR
│   ├── YOLOv3
│   ├── YOLOV4
│   ├── YOLOV4Scaled
│   ├── YOLOV5
│   ├── YOLOV6
│   ├── YOLOV7
│   └── YOLOX
├── LICENSE
├── models
│   ├── deep_sort_pytorch
│   ├── ppyoloe
│   ├── readme.md
│   ├── yolor
│   ├── yolov3
│   ├── yolov4
│   ├── yolov4-608
│   ├── yolov4Scaled
│   ├── yolov5
│   ├── yolov6
│   ├── yolov7
│   └── yolox
├── output
│   ├── ppyoloe
│   ├── README.MD
│   ├── yolor
│   ├── yolov3
│   ├── yolov4
│   ├── yolov4Scaled
│   ├── yolov5
│   ├── yolov6
│   ├── yolov7
│   └── yolox
├── ped_det_server.py
├── README.md
├── requirements.txt
├── results_analysis
│   └── analysis.py
├── scripts
│   ├── yoloe.sh
│   ├── yolor.sh
│   ├── yolov3_deepsort.sh
│   ├── yolov3_tiny_deepsort.sh
│   ├── yolov4_deepsort.sh
│   ├── yolov4Scaled_deepsort.sh
│   ├── yolov5_deepsort.sh
│   ├── yolov6_deepsort.sh
│   ├── yolov7_deepsort.sh
│   └── yolox_deepsort.sh
├── thirdparty
│   ├── fast-reid
│   └── mmdetection
├── train.jpg
├── tutotial
│   ├── Hungarian_Algorithm.ipynb
│   ├── kalman_filter.ipynb
│   └── kalman_filter.py
├── utils
│   ├── asserts.py
│   ├── draw.py
│   ├── evaluation.py
│   ├── __init__.py
│   ├── io.py
│   ├── json_logger.py
│   ├── log.py
│   ├── parser.py
│   ├── __pycache__
│   └── tools.py
├── webserver
│   ├── config
│   ├── images
│   ├── __init__.py
│   ├── readme.md
│   ├── rtsp_threaded_tracker.py
│   ├── rtsp_webserver.py
│   ├── server_cfg.py
│   └── templates
└── yolov3_deepsort_eval.py

Dependencies

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See this requirements.txt for more detail.

• python 3 (python2 not sure)
• numpy
• scipy
• opencv-python
• sklearn
• torch >= 0.4
• torchvision >= 0.1
• pillow
• vizer
• edict

Quick Start

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0. Check all dependencies installed

pip install -r requirements.txt

for user in china, you can specify pypi source to accelerate install like:

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

1. Clone this repository

git clone https://github.com/xuarehere/yolovx_deepsort_pytorch.git

2. Download YOLOv3 parameters

cd detector/YOLOv3/weight/
wget https://pjreddie.com/media/files/yolov3.weights
wget https://pjreddie.com/media/files/yolov3-tiny.weights
cd ../../../

3. Download deepsort parameters ckpt.t7

cd deep_sort/deep/checkpoint
# download ckpt.t7 from
https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder
cd ../../../

4. Compile nms module

cd detector/YOLOv3/nms
sh build.sh
cd ../../..

Notice: If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either gcc version too low or libraries missing.

5. (Optional) Prepare third party submodules

fast-reid

This library supports bagtricks, AGW and other mainstream ReID methods through providing an fast-reid adapter.

to prepare our bundled fast-reid, then follow instructions in its README to install it.

Please refer to configs/fastreid.yaml for a sample of using fast-reid. See Model Zoo for available methods and trained models.

MMDetection

This library supports Faster R-CNN and other mainstream detection methods through providing an MMDetection adapter.

to prepare our bundled MMDetection, then follow instructions in its README to install it.

Please refer to configs/mmdet.yaml for a sample of using MMDetection. See Model Zoo for available methods and trained models.

Run

git submodule update --init --recursive

6. Run demo

usage: deepsort.py [-h]
                   [--fastreid]
                   [--config_fastreid CONFIG_FASTREID]
                   [--mmdet]
                   [--config_mmdetection CONFIG_MMDETECTION]
                   [--config_detection CONFIG_DETECTION]
                   [--config_deepsort CONFIG_DEEPSORT] [--display]
                   [--frame_interval FRAME_INTERVAL]
                   [--display_width DISPLAY_WIDTH]
                   [--display_height DISPLAY_HEIGHT] [--save_path SAVE_PATH]
                   [--cpu] [--camera CAM]
                   VIDEO_PATH         

# yolov3 + deepsort
python deepsort.py [VIDEO_PATH]

# yolov3_tiny + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml

# yolov3 + deepsort on webcam
python3 deepsort.py /dev/video0 --camera 0

# yolov3_tiny + deepsort on webcam
python3 deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0

# fast-reid + deepsort
python deepsort.py [VIDEO_PATH] --fastreid [--config_fastreid ./configs/fastreid.yaml]

# MMDetection + deepsort
python deepsort.py [VIDEO_PATH] --mmdet [--config_mmdetection ./configs/mmdet.yaml]


# yolov4 + deepsort on video
python3 deepsort.py ./001.avi --save_path ./output/yolov4/001 --config_detection ./configs/yolov4.yaml --detect_model yolov4


# yolov4Scaled + deepsort on video
python3 deepsort.py ./001.avi --save_path ./output/yolov4Scaled/001 --config_detection ./configs/yolov4Scaled.yaml --detect_model yolov4Scaled

# yolov5 + deepsort on video
python3 deepsort.py ./001.avi --save_path ./output/yolov5/001 --config_detection ./configs/yolov5.yaml --detect_model yolov5

# yolov6 + deepsort on video
python3 deepsort.py ./001.avi --save_path ./output/yolov6/001 --config_detection ./configs/yolov6.yaml --detect_model yolov6

# yolov7 + deepsort on video
python3 deepsort.py ./001.avi --save_path ./output/yolov7/001 --config_detection ./configs/yolov7.yaml --detect_model yolov7

# yolox + deepsort on video
python deepsort.py  ./001.avi --save_path ./output/yolox/001  --config_detection ./configs/yolox.yaml  --detect_model yolox 

Use --display to enable display.
Results will be saved to ./output/results.avi and ./output/results.txt.

All files above can also be accessed from BaiduDisk!
linker：BaiduDisk passwd：fbuw

Training the Object model

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See this link for more detail

Training the RE-ID model

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The original model used in paper is in original_model.py, and its parameter here original_ckpt.t7.

To train the model, first you need download Market1501 dataset or Mars dataset.

Then you can try train.py to train your own parameter and evaluate it using test.py and evaluate.py.

Train

$ cd ./deep_sort/deep/train.py

$ python train.py --data-dir /workspace/dataset/Market-1501/Market-1501-v15.09.15/pytorch/ --interval 10  --gpu-id 0

See this link for more detail

Demo videos and images

References

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• nwojke/deep_sort
• Joseph Redmon/yolov3
• ZQPei/deep_sort_pytorch
• WongKinYiu/PyTorch_YOLOv4
• WongKinYiu/ScaledYOLOv4
• ultralytics/yolov5
• meituan/YOLOv6
• WongKinYiu/yolov7
• Megvii-BaseDetection/YOLOX
• WongKinYiu/yolor