Name
Title | ||
|---|---|---|
Automatic localization and decoding of honeybee markers using deep convolutional neural networks. |
Abstract | ||
|---|---|---|
The honeybee is a fascinating model animal to investigate how collective behavior emerges from (inter-)actions of thousands of individuals. Bees may acquire unique memories throughout their lives. These experiences affect social interactions even over large time frames. Tracking and identifying all bees in the colony over their lifetimes therefore may likely shed light on the interplay of individual differences and colony behavior. This paper proposes a software pipeline based on two deep convolutional neural networks for the localization and decoding of custom binary markers that honeybees carry from their first to the last day in their life. We show that this approach outperforms similar systems proposed in recent literature. By opening this software for the public, we hope that the resulting datasets will help advancing the understanding of honeybee collective intelligence. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Collective behavior,Convolutional neural network,Computer science,Collective intelligence,Software,Artificial intelligence,Decoding methods,Machine learning,Binary number |
DocType | Volume | Citations |
Journal | abs/1802.04557 | 1 |
PageRank | References | Authors |
0.40 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Wild | 1 | 18 | 3.43 |
| Leon Sixt | 2 | 1 | 1.41 |
| Tim Landgraf | 3 | 1 | 1.41 |