Features for Classifying Insect Trajectories in Event Camera Recordings

Regina Pohle-Fröhlich; Colin Gebler; Marc Böge; Tobias Bolten; Leland Gehlen; Michael Glück; Kirsten Traynor

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Paper

Features for Classifying Insect Trajectories in Event Camera Recordings

Topics: Deep Learning for Visual Understanding ; Features Extraction

In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3 VISAPP: VISAPP, 355-364, 2025 , Porto, Portugal

Authors: Regina Pohle-Fröhlich ¹ ; Colin Gebler ¹ ; Marc Böge ¹ ; Tobias Bolten ¹ ; Leland Gehlen ² ; Michael Glück ² and Kirsten S. Traynor ²

Affiliations: ¹ Institute for Pattern Recognition, Niederrhein University of Applied Sciences, Krefeld, Germany ; ² State Institute of Bee-Research, University of Hohenheim, Stuttgart, Germany

Keyword(s): Event Camera, Classification, Insect Monitoring.

Abstract: Studying the factors that affect insect population declines requires a monitoring system that automatically records insect activity and environmental factors over time. For this reason, we use a stereo setup with two event cameras in order to record insect trajectories. In this paper, we focus on classifying these trajectories into insect groups. We present the steps required to generate a labeled data set of trajectory segments. Since the manual generation of a labelled dataset is very time consuming, we investigate possibilities for label propagation to unlabelled insect trajectories. The autoencoder FoldingNet and PointNet++ as a classification network for point clouds are analyzed to generate features describing trajectory segments. The generated feature vectors are converted to 2D using t-SNE. Our investigations showed that the projection of the feature vectors generated with PointNet++ produces clusters corresponding to the different insect groups. Using the PointNet++ with fu lly-connected layers directly for classification, we achieved an overall accuracy of 90.7% for the classification of insects into five groups. In addition, we have developed and evaluated algorithms for the calculation of the speed and size of insects in the stereo data. These can be used as additional features for further differentiation of insects within groups. (More)

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Paper citation in several formats:

Pohle-Fröhlich, R., Gebler, C., Böge, M., Bolten, T., Gehlen, L., Glück, M. and Traynor, K. S. (2025). Features for Classifying Insect Trajectories in Event Camera Recordings. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 355-364. DOI: 10.5220/0013140100003912

@conference{visapp25,
author={Regina Pohle{-}Fröhlich and Colin Gebler and Marc Böge and Tobias Bolten and Leland Gehlen and Michael Glück and Kirsten S. Traynor},
title={Features for Classifying Insect Trajectories in Event Camera Recordings},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={355-364},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013140100003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Features for Classifying Insect Trajectories in Event Camera Recordings
SN - 978-989-758-728-3
IS - 2184-4321
AU - Pohle-Fröhlich, R.
AU - Gebler, C.
AU - Böge, M.
AU - Bolten, T.
AU - Gehlen, L.
AU - Glück, M.
AU - Traynor, K.
PY - 2025
SP - 355
EP - 364
DO - 10.5220/0013140100003912
PB - SciTePress