Honeybee Segmentation and Tracking Datasets

Here you can find data resulting from our publications on markerless honeybee detection and tracking.

Honeybee detection

This dataset was created as a part of the study Towards dense object tracking in a 2D honeybee hive. It comprises frames and annotations of 2 video recordings of an observation bee hive. The recordings were done at 30 fps and 70 fps, in each of them 360 frames were annotated in a consecutive sequence at 2 fps.

The annotation includes positions and orientation angles of all bees in an image within a selected region with high bee density. Annotating was split into tasks on 1024x1024 px windows as shown below:

30 fps recording
70 fps recording

2 object classes were introduced: (1) fully visible bees, (2) abdomens of bees partially hidden inside cells of a honey comb. The orientation angle of objects of class 2 is always 0. The format of annotation files is:
offset_x offset_y class position_x position_y angle

Files for download:

Due to the size of the images we did not post the full recordings here. If you are interested in other parts of this recording please contact us.

Tutorials

Tutorial with a simplified segmentation-based detection can be found here: https://github.com/oist/DenseObjectDetection. We also provide a checkpoint pretrained on the 30fps dataset.

A parametrizable version of our annotation tool can be found here: https://github.com/oist/DenseObjectAnnotation.

 

Honeybee tracking

This dataset is the result of our study Pixel personality for dense object tracking in a 2D honeybee hive and it comprises detection and trajectory information from two beehive recordings. The videos were originally recorded at 30 fps, we downsampled them to 10 fps 5 min segments. Object detections were done as described in the work above. Detection files are numbered according to the video frames and have format: position_x,position_y,object_class,orientation_angle. Trajectory files comprise ~50% of individuals that were correctly tracked for > 80% of time. The file format is frame_nb,position_x,position_y,object_class,orientation_angle. 

Files for download:

Recording 1:

Recording 2: