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https://github.com/ZQPei/deep_sort_pytorch
MOT using deepsort and yolov3 with pytorch
https://github.com/ZQPei/deep_sort_pytorch
cnn-model deep-sort deepsort mot mot-tracking pytorch sort yolo3 yolov3
Last synced: 12 days ago
JSON representation
MOT using deepsort and yolov3 with pytorch
- Host: GitHub
- URL: https://github.com/ZQPei/deep_sort_pytorch
- Owner: ZQPei
- License: mit
- Created: 2018-09-26T15:34:11.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-06-11T03:16:26.000Z (17 days ago)
- Last Synced: 2024-06-11T04:32:10.292Z (17 days ago)
- Topics: cnn-model, deep-sort, deepsort, mot, mot-tracking, pytorch, sort, yolo3, yolov3
- Language: Python
- Homepage:
- Size: 28.5 MB
- Stars: 2,727
- Watchers: 43
- Forks: 715
- Open Issues: 121
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - ZQPei/deep_sort_pytorch
- awesome-stars - ZQPei/deep_sort_pytorch - MOT using deepsort and yolov3 with pytorch (Python)
README
# 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](https://github.com/ultralytics/yolov5/tree/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](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/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](deep_sort/deep/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](https://arxiv.org/abs/1703.07402) is FasterRCNN , and the original source code is [HERE](https://github.com/nwojke/deep_sort).
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
```bash
pip install -r requirements.txt
```
for user in china, you can specify pypi source to accelerate install like:
```bash
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
```
1. Clone this repository
```bash
git clone [email protected]:ZQPei/deep_sort_pytorch.git
```
2. Download detector parameters
```bash
# 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
```bash
# 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
```bash
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](https://github.com/JDAI-CV/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](https://github.com/JDAI-CV/fast-reid/blob/master/docs/MODEL_ZOO.md) for available methods and trained models.
[MMDetection](https://github.com/open-mmlab/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](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) for available methods and trained models.
Run
```
git submodule update --init --recursive
```
6. Run demo
```bash
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](https://pan.baidu.com/s/1YJ1iPpdFTlUyLFoonYvozg)
passwd：fbuw
## Training the RE-ID model
Check [GETTING_STARTED.md](deep_sort/deep/GETTING_STARTED.md) to start training progress using standard benchmark or **customized dataset**.
## Demo videos and images
[demo.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6)
[demo2.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6)
## References
- paper: [Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402)
- code: [nwojke/deep_sort](https://github.com/nwojke/deep_sort)
- paper: [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
- code: [Joseph Redmon/yolov3](https://pjreddie.com/darknet/yolo/)
- paper: [Mask R-CNN](https://arxiv.org/pdf/1703.06870)
- code: [WZMIAOMIAO/Mask R-CNN](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/mask_rcnn)
- paper: [YOLOv5](https://github.com/ultralytics/yolov5)
- code: [ultralytics/yolov5](https://github.com/ultralytics/yolov5/tree/v6.1)