Honeybee video tracking data

Monitoring animals in their natural habitat is essential for the advancement of animal behavioural studies, especially in pollination studies. We present a novel hybrid detection and tracking algorithm "HyDaT" to monitor unmarked insects outdoors. Our software can detect an insect, identify when a tracked insect becomes occluded from view and when it re-emerges, determine when an insect exits the camera field of view, and our software assembles a series of insect locations into a coherent trajectory. The insect detecting component of the software uses background subtraction and deep learning-based detection together to accurately and efficiently locate the insect. This dataset includes videos of honeybees foraging in two ground-covers Scaevola and Lamb's-ear, comprising of complex background detail, wind-blown foliage, and honeybees moving into and out of occlusion beneath leaves and among three-dimensional plant structures. Honeybee tracks and associated outputs of experiments extracted using HyDaT algorithm are included in the dataset. The dataset also contains annotated images and pre-trained YOLOv2 object detection models of honeybees.

在动物的自然栖息地开展监测工作，对于推动动物行为学研究的发展至关重要，在传粉生物学研究中尤为关键。本研究提出了一种全新的混合检测与跟踪算法「HyDaT」，用于户外无标记昆虫的监测。该软件可实现昆虫检测，识别被跟踪昆虫被遮挡以及重新进入视野的时刻，同时可判定昆虫离开摄像机视场的时机，并能将一系列昆虫定位数据整合为连贯的运动轨迹。软件的昆虫检测模块结合了背景减法与基于深度学习的检测技术，能够精准且高效地完成昆虫定位任务。本数据集包含蜜蜂在两种地被植物——草海桐（Scaevola）与绵毛水苏（Lamb's-ear）中觅食的视频素材，这些视频的背景细节繁杂，涵盖随风晃动的枝叶，以及在叶片下方进出遮挡状态、穿梭于三维植物结构间的蜜蜂活动画面。数据集还收录了通过HyDaT算法提取得到的蜜蜂运动轨迹与相关实验输出结果，同时包含标注后的图像以及针对蜜蜂预训练的YOLOv2目标检测模型。