GitHub - ZQPei/deep_sort_pytorch: MOT tracking using deepsort and yolov3 with py...
source link: https://github.com/ZQPei/deep_sort_pytorch
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.md
Deep Sort with PyTorch
Update(1-1-2020)
Changes
- fix bugs
- refactor code
- accerate detection by adding nms on gpu
Latest 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.
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 >= 0.4
- torchvision >= 0.1
- pillow
- vizer
- edict
Quick Start
- 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
- Clone this repository
git clone [email protected]:ZQPei/deep_sort_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
cd detector/YOLOv3/nms sh build.sh cd ../../..
- Run demo
usage: python yolov3_deepsort.py VIDEO_PATH
[--help]
[--frame_interval FRAME_INTERVAL]
[--config_detection CONFIG_DETECTION]
[--config_deepsort CONFIG_DEEPSORT]
[--ignore_display]
[--display_width DISPLAY_WIDTH]
[--display_height DISPLAY_HEIGHT]
[--save_path SAVE_PATH]
[--cpu]
# yolov3 + deepsort
python yolov3_deepsort.py [VIDEO_PATH]
# yolov3_tiny + deepsort
python yolov3_deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml
If you dont support X server, use --ignore_display
to disable display.
Results will be saved to ./demo/demo.avi
.
All files above can also be accessed from BaiduDisk!
linker:BaiduDisk
passwd:fbuw
Training the RE-ID model
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.
Demo videos and images
References
-
paper: Simple Online and Realtime Tracking with a Deep Association Metric
-
code: nwojke/deep_sort
-
paper: YOLOv3
-
code: Joseph Redmon/yolov3
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK