Table of contents
Deep Sort with PyTorch(yolo-all)
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
</details>
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
</details>
Quick Start
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Check all dependencies installed
pip install-rrequirements.txt
for user in china, you can specify pypi source to accelerate install like:
pip install-rrequirements.txt-ihttps://pypi.tuna.tsinghua.edu.cn/simple
Clone this repository
git clone https://github.com/xuarehere/yolovx_deepsort_pytorch.git
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 ../../../
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 ../../../
Compile nms module
cddetector/YOLOv3/nms
shbuild.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.
(Optional) Prepare third party submodules
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.
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
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：BaiduDiskpasswd：fbuw
</details>
Training the Object model
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</details>
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
</details>
Demo videos and images
# References
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</details>
