Bytetrack starter guide

This repo is a packaged version of the ByteTrack algorithm.

ByteTrack is a multi-object tracking computer vision model. Using ByteTrack, you can allocate IDs for unique objects in a video for use in tracking objects.

Installation

pip install git+https://github.com/artefactory-fr/bytetrack.git@main

Detection object with Bytetracker and YOLO

from bytetracker import BYTETracker
tracker = BYTETracker(args)
for frame_id, image_filename in enumerate(frames):
    img = cv2.imread(image_filename)
    detections = your_model.predict(img)
    tracked_objects = tracker.update(detections, frame_id)

Copyright

Copyright (c) 2022 Kadir Nar

ByteTrack License

ByteTrack is licensed under the MIT License. See the LICENSE file and the ByteTrack repository for more information.

Citation

@article{zhang2022bytetrack,
  title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
  author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Weng, Fucheng and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2022}
}

@video{intel-iot-devkit,
  author={Intel IoT},
  title={Intel IoT Devkit Sample Videos(https://github.com/intel-iot-devkit/sample-videos)},
  license={Creative Commons Attribution 4.0 International License(https://creativecommons.org/licenses/by/4.0/)}