AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture

Paper | Dataset | Weights | Video

Repository for the paper AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture, accepted at ICRA 2024. We propose a novel Multiple Object Tracking (MOT) algorithm specific for usage in precision agriculture. We also present a novel dataset on multiple objects tracking in the context of precision agriculture based on table grapes captured using a RealSense d435i camera.

AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture
Leonardo Saraceni, Ionut M. Motoi, Daniele Nardi, Thomas A. Ciarfuglia https://arxiv.org/abs/2309.13393 (Temporary, accepted to ICRA 2024, soon to appear)

Setup:

To test the tool is necessary to clone the repository and install the required dependencies.

• Python dependencies:

  pip3 install -r requirements.txt
  

• Clone YOLOv5 repository:

  git clone https://github.com/ultralytics/yolov5  # clone
  cd yolov5
  pip install -r requirements.txt  # install
  

• Download detection weights:

  Download weights from the Link:

  Weights

  Alternatively you can download them via command line:

  gdown --folder --remaining-ok https://drive.google.com/drive/folders/1Kl3srt2J0u48Gyx6M1YvrqMnCTNrQz-W?usp=drive_link
  

Use tracker:

We provide a small demo to :

• Download dataset (Optional):

  Download the dataset from the Link:

  Dataset

  Alternatively you can download them via command line:

  gdown --folder --remaining-ok https://drive.google.com/drive/folders/1Kl3srt2J0u48Gyx6M1YvrqMnCTNrQz-W?usp=drive_link
  

• Run tracker:

  By default the tracker runs on the CloseUp1 sequence.

  python3 agriSORT.py
  

  To visualize all the possible settings, visualize help:

  python3 agriSORT.py --help
  

Citation

@misc{saraceni2023agrisort,
      title={AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture}, 
      author={Leonardo Saraceni and Ionut M. Motoi and Daniele Nardi and Thomas A. Ciarfuglia},
      year={2023},
      eprint={2309.13393},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}