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},
}