Deep Sort with PyTorch

Update(1-1-2020)

Changes

• fix bugs
• refactor code
• accerate detection by adding nms on gpu

Update(07-22)

Changes

• bug fix (Thanks @JieChen91 and @yingsen1 for bug reporting).
• using batch for feature extracting for each frame, which lead to a small speed up.
• code improvement.

Futher improvement direction

• Train detector on specific dataset rather than the official one.
• Retrain REID model on pedestrain dataset for better performance.
• Replace YOLOv3 detector with advanced ones.

Update(23-05-2024)

tracking

• Added resnet network to the appearance feature extraction network in the deep folder

• Fixed the NMS bug in the preprocessing.py and also fixed covariance calculation bug in the kalmen_filter.py in the sort folder

detecting

• Added YOLOv5 detector, aligned interface, and added YOLOv5 related yaml configuration files. Codes references this repo: YOLOv5-v6.1.

• The train.py, val.py and detect.py in the original YOLOv5 were deleted. This repo only need yolov5x.pt.

deepsort

• Added tracking target category, which can display both category and tracking ID simultaneously.

Update(28-05-2024)

segmentation

• Added Mask RCNN instance segmentation model. Codes references this repo: mask_rcnn. Visual result saved in demo/demo2.gif.
• Similar to YOLOv5, train.py, validation.py and predict.py were deleted. This repo only need maskrcnn_resnet50_fpn_coco.pth.

deepsort

• Added tracking target mask, which can display both category, tracking ID and target mask simultaneously.

latest Update(09-06-2024)

feature extraction network

• Using nn.parallel.DistributedDataParallel in PyTorch to support multiple GPUs training.
• Added GETTING_STARTED.md for better using train.py and train_multiGPU.py.

Updated README.md for previously updated content(#Update(23-05-2024) and #Update(28-05-2024)).

Any contributions to this repository is welcome!

Introduction

This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in PAPER is FasterRCNN , and the original source code is HERE.
However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use YOLOv3 to generate bboxes instead of FasterRCNN.

Dependencies

• python 3 (python2 not sure)
• numpy
• scipy
• opencv-python
• sklearn
• torch >= 1.9
• torchvision >= 0.13
• pillow
• vizer
• edict
• matplotlib
• pycocotools
• tqdm

Quick Start

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 git@github.com:ZQPei/deep_sort_pytorch.git

2. Download detector parameters

# if you use YOLOv3 as detector in this repo
cd detector/YOLOv3/weight/
wget https://pjreddie.com/media/files/yolov3.weights
wget https://pjreddie.com/media/files/yolov3-tiny.weights
cd ../../../

# if you use YOLOv5 as detector in this repo
cd detector/YOLOv5
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
or 
wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt
cd ../../

# if you use Mask RCNN as detector in this repo
cd detector/Mask_RCNN/save_weights
wget https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
cd ../../../

3. Download deepsort feature extraction networks weight

# if you use original model in PAPER
cd deep_sort/deep/checkpoint
# download ckpt.t7 from
https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder
cd ../../../

# if you use resnet18 in this repo
cd deep_sort/deep/checkpoint
wget https://download.pytorch.org/models/resnet18-5c106cde.pth
cd ../../../

4. (Optional) Compile nms module if you use YOLOv3 as detetor in this repo

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] --config_detection ./configs/yolov3.yaml

# 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

# yolov5s + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov5s.yaml

# yolov5m + deepsort
python deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov5m.yaml

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

# 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]

Use --display to enable display image per frame.
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 RE-ID model

Check GETTING_STARTED.md to start training progress using standard benchmark or customized dataset.

Demo videos and images

demo.avi demo2.avi

References

• paper: Simple Online and Realtime Tracking with a Deep Association Metric
• code: nwojke/deep_sort
• paper: YOLOv3: An Incremental Improvement
• code: Joseph Redmon/yolov3
• paper: Mask R-CNN
• code: WZMIAOMIAO/Mask R-CNN
• paper: YOLOv5
• code: ultralytics/yolov5