PanAfrican2020
(please cite this paper when using the dataset: F Sakib, T Burghardt. Visual Recognition of Great Ape Behaviours in the Wild. Proc. 25th IEEE/IAPR International Conference on Pattern Recognition (ICPR) Workshop on Visual Observation and Analysis of Vertebrate And Insect Behavior (VAIB), January 2021.)

INTRODUCTION AND DESCRIPTION
This dataset contains approx. 180,000 XML annotation files assigning behaviours (9 overall behaviours), animal bounding boxes and species names to every frame across the 500 videos of the PanAfrican2019 dataset captured at 24 frames per seconds. Note that this dataset does not contain the video data itself, which is held by the PanAfrican Programme (see Acknowledgement below). Use standard XML extraction software for porting the dataset to other formats. Example source code for reading and utilising the dataset to learn automatic behaviour classification via deep learning can be found at https://github.com/fznsakib/great-ape-behaviour-detector

ANNOTATION DATA
The annotations of the dataset are in XML format. There are 180,000 annotations, one per video frame. The format first specifies the videoname, i.e. the main filename of the related MP4 file in the PanAfrican2019 dataset. Secondly, the video frame within this video file is specified. Next, the frame resolution is given via width and height in pixels, and depth as the number of channels (usually 3 for RGB). Finally, the various animals in the frame are each described as one object. Every object has a category (usually 'Great Ape') and name (usually 'chimpanzee' or 'gorilla') followed by the bounding box that contains the animal in the frame and the behaviour (out of the 9 possible) the animal exhibits.   
An example can be seen below:
<annotation>
  <videoname>adXBSoAug0</videoname>
  <frameid>4</frameid>
  <size>
    <height>404</height>
    <width>720</width>
    <depth>3</depth>
  </size>
  <object>
    <id>0</id>
    <category>Great Ape</category>
    <name>chimpanzee</name>
    <bndbox>
      <xmin>675</xmin>
      <ymin>28</ymin>
      <xmax>716</xmax>
      <ymax>105</ymax>
    </bndbox>
    <activity>climbing_down</activity>
  </object>
</annotation>

ACKNOWLEDGEMENT
We would like to thank the entire team of the Pan African Programme: ‘The Cultured Chimpanzee’ and its collaborators for allowing the use of their data for the paper associated to this dataset. Please contact the copyright holder Pan African Programme at http://panafrican.eva.mpg.de to obtain the videos underpinning the dataset. Particularly, we thank: H Kuehl, C Boesch, M Arandjelovic, and P Dieguez. We would also like to thank: K Zuberbuehler, K Corogenes, E Normand, V Vergnes, A Meier, J Lapuente, D Dowd, S Jones, V Leinert, EWessling, H Eshuis, K Langergraber, S Angedakin, S Marrocoli, K Dierks, T C Hicks, J Hart, K Lee, and M Murai. Thanks also to the team at https://www.chimpandsee.org. The work that allowed for the collection of the dataset was funded by the Max Planck Society, Max Planck Society Innovation Fund, and Heinz L. Krekeler. In this respect we would also like to thank: Foundation Ministre de la Recherche Scientifique, and Ministre des Eaux et Forlts in Cote d’Ivoire; Institut Congolais pour la Conservation de la Nature and Ministre de la Recherch Scientifique in DR Congo; Forestry Development Authority in Liberia; Direction des Eaux, Forlts Chasses et de la Conservation des Sols, Senegal; and Uganda National Council for Science and Technology, Uganda Wildlife Authority, National
Forestry Authority in Uganda.