Ant Wars: Ant Tracking with Computer Vision

Computer vision is a technique for extracting data from images and videos with wide applications across many fields including medicine, sports analysis, and agriculture. In this project, we are using computer vision to learn about the impacts of invasive ants on native ecosystems. Invasive Argentine ants have spread across the globe, wiping out native ants such as the California harvester ant, which helps spread seeds and maintain a diverse plant community. To understand how Argentine ants may be affecting harvester ants, we have been documenting the daily rhythm of harvester ant activity around the nest using field cameras programmed to take short videos throughout the day. 

To gather useful data from these videos in an efficient way, we have been developing a custom-built ant tracking software pipeline written in Python with OpenCV which detects harvester ants against a background of dirt and pebbles, then uses these detections to construct trajectories of individual ants, and finally counts the number of ants entering and leaving the nest in four different directions. The next steps will be to test, improve, and validate the software pipeline. This will allow us to process hundreds of videos and gather useful behavioral data.

Students working on this project will (1) assess performance of current methods on existing videos, (2) modify code to improve performance, (3) run code on newly collected videos, and (4) analyze and visualize results.

Name of research group, project, or lab
HMC Bee Lab
Why join this research group or lab?

You will be part of a team of students working on an interdisciplinary project, using biology, mathematics, computation and engineering to solve problems of biological and applied conservation interest. You'll also have the opportunity to spend some time outdoors, observing nature if that is of interest. The variety of techniques and approaches will give you an opportunity to explore your interests and develop new skills. This is a collaborative project with researchers at nearby universities including UC San Diego and UC Riverside, which could expand your professional network. There may be opportunities to continue the work in a senior thesis, present at a regional or national conference, and/or co-author future publications stemming from ongoing work.

Logistics Information:
Project categories
Biology
Computer Science
Computer Vision
Data Science
Ecology
Student ranks applicable
Sophomore
Junior
Senior
Student qualifications

Strong programming experience in Python is required.

Here is a list of other skills/interests that could be useful, or which you might develop along the way. They're not requirements, but if you already have experience or interest in any of them, be sure to mention it in your application.

  • Linux/UNIX command line and shell scripting, high performance computing
  • Computer vision and image processing, e.g. using OpenCV
  • Data science and visualization, particularly with R
  • Ecology and animal behavior
Time commitment
Fall - Part Time
Compensation
Academic Credit
Number of openings
1
Techniques learned

In this project, you will

  • contribute to a software pipeline written in Python
  • test and optimize common computer vision tasks such as object recognition and tracking
  • develop and deploy an efficient workflow for running code on many videos
  • extract, analyze and visualize data resulting from the pipeline

You will also learn to read and discuss scientific literature, and to communicate across disciplinary boundaries and with the public about your work.

 

Project start
Fall 2026
Contact Information:
Mentor
mdonaldsonmatasci@hmc.edu
Professor of Biology
Name of project director or principal investigator
Matina Donaldson-Matasci
Email address of project director or principal investigator
mdonaldsonmatasci@g.hmc.edu
1 sp. | 0 appl.
Time commitment
Fall - Part Time
Project categories
Data Science (+4)
BiologyComputer ScienceComputer VisionData ScienceEcology