Abstract
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.
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Reza, M.A., Marker, J., Mhatre, S., Saunders, A., Marenda, D., Breen, D. (2012). Video Analysis Algorithms for Automated Categorization of Fly Behaviors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_23
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DOI: https://doi.org/10.1007/978-3-642-33191-6_23
Publisher Name: Springer, Berlin, Heidelberg
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