FEATURES FOR CLASSIFYING INSECT TRAJECTORIES IN EVENT CAMERA RECORDINGS
NIEDERRHEIN UNIVERSITY OF APPLIED SCIENCES & UNIVERSITY OF HOHENHEIM
Regina Pohle-Frohlich, Colin Gebler, Marc Boge, Tobias Bolten, Leland Gehlen, Michael Gluck, Kirsten S. Traynor
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 fully-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.
