Long-duration animal tracking in difficult lighting conditions.
Abstract
High-throughput analysis of animal behavior requires software to analyze videos. Such
software typically depends on the experiments' being performed in good lighting conditions,
but this ideal is difficult or impossible to achieve for certain classes of experiments.
Here, we describe techniques that allow long-duration positional tracking in difficult
lighting conditions with strong shadows or recurring "on"/"off" changes in lighting.
The latter condition will likely become increasingly common, e.g., for Drosophila
due to the advent of red-shifted channel rhodopsins. The techniques enabled tracking
with good accuracy in three types of experiments with difficult lighting conditions
in our lab. Our technique handling shadows relies on single-animal tracking and on
shadows' and flies' being accurately distinguishable by distance to the center of
the arena (or a similar geometric rule); the other techniques should be broadly applicable.
We implemented the techniques as extensions of the widely-used tracking software Ctrax;
however, they are relatively simple, not specific to Drosophila, and could be added
to other trackers as well.
Type
Journal articlePermalink
https://hdl.handle.net/10161/10574Published Version (Please cite this version)
10.1038/srep10432Publication Info
Stern, Ulrich; Zhu, Edward Y; He, Ruo; & Yang, Chung-Hui (2015). Long-duration animal tracking in difficult lighting conditions. Sci Rep, 5. pp. 10432. 10.1038/srep10432. Retrieved from https://hdl.handle.net/10161/10574.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
Collections
More Info
Show full item recordScholars@Duke
Rebecca Chung-Hui Yang
Associate Professor of Neurobiology
Our lab is interested in understanding the neural basis of simple decision-making
processes. We use Drosophila egg-laying site selection as our model system. To understand
how the Drosophila brain assesses and ranks the values of egg-laying options, we use
a combined approach that includes high-throughput optogenetics-based behavioral screen,
automated (machine vision) behavioral tracking of single animals, molecular genetic
tools to identify critical circuit compone

Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info