Abstract
Quantification of behavior is one of the primary requirements to study animal behavior scientifically. Traditionally, behavior has been quantified by manually observing the focal animal(s) across a spatio-temporal scale and recording the occurrences of behavioral events. These events are generally deduced from the movement of different body parts of the animal, typical body postures as well as its overall movement. While collecting data by manual observation has several advantages, it is prone to disadvantages like human bias and being imprecise. Though modern videography has improved the observation and recording of behavior, extracting behavioral data from these video data remained challenging until now.
In this talk, I will discuss how the recent progress in machine learning tools has enabled me, a biologist interested in social insects, to extract behavioral data from videos. I will talk in detail about such a tool, called DeepLabCut, which tracks the movement of individual parts of an animal with minimal human input. I will end the talk with an example of the application of this tool in my current projects, which is understanding the evolution of cooperation and foraging strategies in the Carpenter ants.
Event Details
Date/Time:
-
Date:Wednesday, October 9, 2019 - 12:00pm to 1:00pm
Location:
Howey School of Physics N110
For More Information Contact
Prof. David Hu
