Multi-object trackers in Python

Various multi-object tracking algorithms.

YOLOv3 + CentroidTracker	TF-MobileNetSSD + CentroidTracker
Video source: link	Video source: link

Available Multi Object Trackers

CentroidTracker
IOUTracker
CentroidKF_Tracker
SORT

Available OpenCV-based object detectors:

detector.TF_SSDMobileNetV2
detector.Caffe_SSDMobileNet
detector.YOLOv3

Installation

Pip install for OpenCV (version 3.4.3 or later) is available here and can be done with the following command:

git clone https://github.com/adipandas/multi-object-tracker
cd multi-object-tracker
pip install -e .

Note - for using neural network models with GPU

For using the opencv dnn-based object detection modules provided in this repository with GPU, you may have to compile a CUDA enabled version of OpenCV from source.

For building opencv from source, you can refer the following: [link-1], [link-2]

How to use?: Examples

Please refer examples folder of this repository. You can clone and run the examples as shown in the readme inside the examples folder.

Pretrained object detection models

You will have to download the pretrained weights for the neural-network models. The shell scripts for downloading these are provided in examples folder.
Please refer DOWNLOAD_WEIGHTS.md for more details.

References and Credits

Please see REFERENCES.md

Citation

If you use this repository in your work, please consider citing it with:

@misc{multiobjtracker_amd2018,
  author = {Deshpande, Aditya M.},
  title = {Multi-object trackers in Python},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/adipandas/multi-object-tracker}},
}

@software{aditya_m_deshpande_2020_3951169,
  author       = {Aditya M. Deshpande},
  title        = {Multi-object trackers in Python},
  month        = jul,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.3951169},
  url          = {https://doi.org/10.5281/zenodo.3951169}
}