UC San Diego

A major limitation on performing detailed behavioral analysis of honey bee colonies is that there is currently no efficient way to carry it out. Due to the time required in manually analyzing the data, the current approach and small sample sizes limit the statistical power of these analyses. An automated system can provide a breakthrough in the way this research is performed. Waggle dances are an important aspect of understanding the behavior of honey bees as it serves as a way to communicate among themselves. In this thesis, we develop an automated system using computer vision and learning techniques to solve two problems i) Single bee tracking and waggle detection and ii) Multiple bee waggle detection. Our approach shows that it is possible to train learning algorithms to detect when and where a waggle happens in the hive.