The fruit fly, Drosophila melanogaster, is a well established model organism used to study the mechanisms of both learning and memory in vivo. This paper presents video analysis algorithms that generate data that may be used to categorize fly behaviors. The algorithms aim to replace and improve a labor-intensive, subjective evaluation process with one that is automated, consistent and reproducible; thus allowing for robust, high-throughput analysis of large quantities of video data. The method includes tracking the flies, computing geometric measures, constructing feature vectors, and grouping the specimens using clustering techniques. We also generated a Computed Courtship Index (CCI), a computational equivalent of the existing Courtship Index (CI). The results demonstrate that our automated analysis provides a numerical scoring of fly behavior that is similar to the scoring produced by human observers. They also show that we are able to automatically differentiate between normal and defective flies via analysis of their videotaped movements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.