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, OCSORT, StrongSORT, ByteTrack and BoTSORT, . They can track any object that your Yolov8 model was trained to detect.

pip install -r requirements.txt

$ python track.py

• Detection mode

  $ python track.py --yolo-weights yolov8n.pt      # bboxes only
                                  yolov8n-seg.pt  # bboxes + segmentation masks

• Tracking methods

  $ python track.py --tracking-method ocsort
                                      deepocsort
                                      strongsort
                                      bytetrack
                                      botsort

• Tracking 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

• Select 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 Yolov8 family model for automatic download. You can also select image size of 640 or 1280.

  $ python track.py --source 0 --yolo-weights yolov8n.pt --img 640
                                              yolov8s.pt
                                              yolov8m.pt
                                              yolov8l.pt 
                                              yolov8x.pt --img 1280
                                              ...

• 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  # Track cats and dogs, only

  Here 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