Full description
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.
Issued: 2020-08-31
Created: 2020-08-31
Subjects
Agriculture, Land and Farm Management not elsewhere classified |
Artificial Intelligence and Image Processing |
Deep Learning Applications |
Ecological Applications not elsewhere classified |
Honeybees |
Pollination |
Tracking data |
computer vision algorithms |
video activity data |
User Contributed Tags
Login to tag this record with meaningful keywords to make it easier to discover
Identifiers
- DOI : 10.26180/5F4C8D5815940
