Detecting and tracking honeybees in 3D at the beehive entrance using stereo vision

In response to recent needs of biologists, we lay the foundations for a real-time stereo vision-based system for monitoring flying honeybees in three dimensions at the beehive entrance. Tracking bees is a challenging task as they are numerous, small, and fast-moving targets with chaotic motion. Contrary to current state-of-the-art approaches, we propose to tackle the problem in 3D space. We present a stereo vision-based system that is able to detect bees at the beehive entrance and is sufficiently reliable for tracking. Furthermore, we propose a detect-before-track approach that employs two innovating methods: hybrid segmentation using both intensity and depth images, and tuned 3D multi-target tracking based on the Kalman filter and Global Nearest Neighbor. Tests on robust ground truths for segmentation and tracking have shown that our segmentation and tracking methods clearly outperform standard 2D approaches.