Towards a Dynamic Vision Sensor-based Insect Camera Trap

This paper introduces a visual real-time insect monitoring approach capable of detecting and tracking tiny and fast-moving objects in cluttered wildlife conditions using an RGB-DVS stereo-camera system. By building on the intrinsic benefits of event vision data acquisition, we demonstrate that insect presence can be detected at an extremely high temporal rate (on average more than 40 times real-time) while surpassing the spatial and spectral sensitivity of conventional colour-based sensing. Our DVS-based detection and tracking algorithm extracts insect locations over time, and we evaluated our system based on 81104 manually annotated stereo-frames with 34453 insect appearances featuring highly varying scenes and imaging conditions (including clutter, wind-induced motion, etc.). Comparing our algorithm to two state-of-the-art deep learning algorithms reveals superior results in both detection performance and computational speed. Using the DVS as a trigger for the temporally synchronised RGB camera, we are able to correctly identify 73% of images with and without insects which can be increased to 76% with parameters optimised for different scenes. Overall, our study suggests that DVS-based sensing can be used for visual insect monitoring by enabling reliable real-time insect detection in wildlife conditions while significantly reducing the necessity for data storage, manual labour and energy.