Detection of Honey Bee Dancers and Followers with Model-Based Machine Learning
The waggle dance patterns of the western honey bee are well understood and researched. New methods in computer vision systems allow long-term tracking of individual bees to capture the spatiotemporal position of each bee in the hive. Previous research on the detection of the waggle dance primarily focuses on videos with a high frame rate. The few currently known methods on spatiotemporal date require a high temporal resolution of at least 13 Hz to capture the waggle behavior part. Contrary to the dancer, research on the detection of waggle dance followers is not existent.
This thesis introduces a new model to detect waggle-dancers and their followers in spatiotemporal data with a low temporal resolution by using domain knowledge to engineer specific features that match these behaviors. We describe the model of the waggle-dance and their followers, the patterns behind it and the process to utilize this knowledge into specific features.
The proposed model allows the discovery of not only the waggle-dancers but its followers; the combination of long-term tracking and the detection enables further research into the relation between each bee in the colony.