DVS-StereoInsect: An Event-Based Stereo Dataset for Foreground-Background Insect Segmentation

Insect monitoring is a field of growing importance as the need to evaluate the effects of various stressors on insect populations rises. Existing methods for insect monitoring are often unsuitable for continuous monitoring of larger areas. This work presents an approach that aims to separate insect trajectories from background information in dynamic vision sensor (DVS) recordings on a new dataset. The dataset consists of approximately one hour of training data and six one-minute-long test sets of varying difficulty. A method to synthetically generate foreground-background segmentation-labeled data for this task given appropriately created source recordings is presented. To demonstrate the performance of the approach, an existing method for insect tracking in DVS data and a U-Net-based method are evaluated. The U-Net method achieves a Matthews correlation coefficient (MCC) of 0.955 detecting wasps and 0.850 detecting varied insects in front of a complex natural background. The evaluation of the existing method on the new dataset shows that it is not applicable in all use cases.