This repository contains a highly configurable two-stage-tracker that adjusts to different deployment scenarios. It can jointly perform multiple object tracking and instance segmentation (MOTS). The detections generated by YOLOv8, a family of object detection architectures and models pretrained on the COCO dataset, are passed to the tracker of your choice. Supported ones at the moment are: DeepOCSORT OSNet, BoTSORT OSNet, StrongSORT OSNet, OCSORT and ByteTrack. They can track any object that your Yolov8 model was trained to detect.
Everything is designed with simplicity and flexibility in mind. We don't hyperfocus on results on a single dataset, we prioritize real-world results. If you don't get good tracking results on your custom dataset with the out-of-the-box tracker configurations, use the evolve.py script for tracker hyperparameter tuning.
git clone --recurse-submodules https://github.com/mikel-brostrom/yolov8_tracking.git # clone recursively
cd yolov8_tracking
pip install -r requirements.txt # install dependencies
Tutorials
Experiments
In inverse chronological order:
Custom object detection architecture
The trackers provided in this repo can be used with other object detectors than Yolov8. Make sure that the output of your detector has the following format:
(x1,y1, x2, y2, obj, cls0, cls1, ..., clsn)pass this directly to the tracker here:
$ python track.py --yolo-weights yolov8n.pt # bboxes only
yolov8n-seg.pt # bboxes + segmentation masksTracking methods
$ python track.py --tracking-method deepocsort
strongsort
ocsort
bytetrack
botsortTracking sources
Tracking can be run on most video formats
$ python track.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streamSelect Yolov8 model
There is a clear trade-off between model inference speed and overall performance. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download. These model can be further optimized for you needs by the export.py script
$ python track.py --source 0 --yolo-weights yolov8n.pt --img 640
yolov8s.tflite
yolov8m.pt
yolov8l.onnx
yolov8x.pt --img 1280
...Select ReID model
Some tracking methods combine appearance description and motion in the process of tracking. For those which use appearance, you can choose a ReID model based on your needs from this ReID model zoo. These model can be further optimized for you needs by the reid_export.py script
$ python track.py --source 0 --reid-weights osnet_x0_25_market1501.pt
mobilenetv2_x1_4_msmt17.engine
resnet50_msmt17.onnx
osnet_x1_0_msmt17.pt
...Filter tracked classes
By default the tracker tracks all MS COCO classes.
If you want to track a subset of the classes that you model predicts, add their corresponding index after the classes flag,
python track.py --source 0 --yolo-weights yolov8s.pt --classes 16 17 # COCO yolov8 model. Track cats and dogs, onlyHere is a list of all the possible objects that a Yolov8 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero
Updates with predicted-ahead bbox in StrongSORT
If your use-case contains many occlussions and the motion trajectiories are not too complex, you will most certainly benefit from updating the Kalman Filter by its own predicted state. Select the number of predictions that suits your needs here:
Save the trajectories to you video by:
python track.py --source ... --save-trajectories --save-vid
MOT compliant results
Can be saved to your experiment folder runs/track/<yolo_model>_<deep_sort_model>/ by
python track.py --source ... --save-txtTracker hyperparameter tuning
We use a fast and elitist multiobjective genetic algorithm for tracker hyperparameter tuning. By default the objectives are: HOTA, MOTA, IDF1. Run it by
$ python evolve.py --tracking-method strongsort --benchmark MOT17 --n-trials 100 # tune strongsort for MOT17
--tracking-method ocsort --benchmark <your-custom-dataset> --objective HOTA # tune ocsort for maximizing HOTA on your custom tracking datasetThe set of hyperparameters leading to the best HOTA result are written to the tracker's config file.
For Yolov8 tracking bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: [email protected]