GitHub - mikel-brostrom/Yolov5_DeepSort_Pytorch: Real-time multi-object tracker...
source link: https://github.com/mikel-brostrom/Yolov5_DeepSort_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.
Yolov5 + Deep Sort with PyTorch
Introduction
This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algorithm which tracks the objects. It can track any object that your Yolov5 model was trained to detect.
Tutorials
Before you run the tracker
- Clone the repository recursively:
git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git
If you already cloned and forgot to use --recurse-submodules
you can run git submodule update --init
- Make sure that you fulfill all the requirements: Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install, run:
pip install -r requirements.txt
Tracking sources
Tracking can be run on most video formats
python3 track.py --source ... --show-vid # show live inference results as well
- Video:
--source file.mp4
- Webcam:
--source 0
- RTSP stream:
--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa
- HTTP stream:
--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg
Select a Yolov5 family model
There is a clear trade-off between model inference speed and accuracy. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download
python3 track.py --source 0 --yolo_weights yolov5s.pt --img 640 # smallest yolov5 family model
python3 track.py --source 0 --yolo_weights yolov5x6.pt --img 1280 # largest yolov5 family model
Filter tracked classes
By default the tracker tracks all MS COCO classes.
If you only want to track persons I recommend you to get these weights for increased performance
python3 track.py --source 0 --yolo_weights yolov5/weights/crowdhuman_yolov5m.pt --classes 0 # tracks persons, only
If you want to track a subset of the MS COCO classes, add their corresponding index after the classes flag
python3 track.py --source 0 --yolo_weights yolov5s.pt --classes 16 17 # tracks cats and dogs, only
Here is a list of all the possible objects that a Yolov5 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero.
MOT compliant results
Can be saved to inference/output
by
python3 track.py --source ... --save-txt
If you find this project useful in your research, please consider cite:
@misc{yolov5deepsort2020, title={Real-time multi-object tracker using YOLOv5 and deep sort}, author={Mikel Broström}, howpublished = {\url{https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch}}, year={2020} }
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK