Dataset Open Access

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

Nam Cao; Matthias Meyer; Lothar Thiele; Olga Saukh

Dataset description

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

  • Pollen images of 16 types:

    • 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

    • 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].
    • Cropped file name structure [Video file name]_[TrackingID]_[Image index of a grain]_[Frame index in video]
      • Example, if a grain has 5 images, the file name would be:
  • Field data over 3 days are gathered in Graz in spring 2020.

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


  • opencv
  • numpy
  • matplotlib


[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.

  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)},
  month = {2},	
  year = {2020},
Appears in the Proceedings of the 3rd Workshop on Data Acquisition To Analysis (DATA '20)
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