Automated behavioral phenotype detection and analysis using color-based motion tracking

The problem of elucidating the functional significance of genes is a key challenge of modern science. Solving this problem can lead to fundamental advancements across multiple areas such starting from pharmaceutical drug discovery to agricultural sciences. A commonly used approach in this context involves studying genetic influence on model organisms. These influences can be expressed at behavioral, morphological, anatomical, or molecular levels and the expressed patterns are called phenotypes. Unfortunately, detailed studies of many phenotypes, such as the behavior of an organism, is highly complicated due to the inherent complexity of the phenotype pattern and because of the fact that it may evolve over long time periods. In this paper, we propose applying color-based tracking to study Ecdysis in the hornworm - a biologically highly relevant phenotype whose complexity had thus far, prevented application of automated approaches. We present experimental results which demonstrate the accuracy of tracking and phenotype determination under conditions of complex body movement, partial occlusions, and body deformations. A key additional goal of our paper is to expose the computer vision community to such novel applications, where techniques from vision and pattern analysis can have a seminal influence on other branches of modern science.