Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks

This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.

• Pollen images of 16 types: images_16_types.zip

  • Acer Pseudoplatanus
  • Aesculus Carnea
  • Alnus
  • Anthoxanthum
  • Betula Pendula
  • Brassica
  • Carpinus
  • Corylus
  • Dactylis Glomerata
  • Fraxinus
  • Pinus Nigra
  • Platanus
  • Populus Nigra
  • Prunus Avium
  • Sequoiadendron Giganteum
  • Taxus Baccata

• Pollen video library pollen_video_library.zip

  • Each type of pollen is in a separate folder, there may be multiple videos per type.
  • In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1].

• Field data over 3 days are gathered in Graz in spring 2020. pollen_field_data.zip

• Sample code to load the data and visualize the images is in plot_pollen_sample.py. Download and extract the file images_16_types.zip in the same folder as plot_pollen_sample.py to run the example.

Dependecies

• opencv
• numpy
• matplotlib

Credit

[1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119.

@inproceedings{namcao2020pollen,
  title = {Automated Pollen Detection with an Affordable Technology},
  author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh},
  booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)},
  pages={108–119}
  month = {2},	
  year = {2020},
}