Deep Chimpact: Depth Estimation for Wildlife Conservation Hosted By MathWorks


An image of a chimpanzee, and a model-generated depth mask for the image


Healthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change.

Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size.

However, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to monitor wildlife populations.

Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time.

Along the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!

Competition End Date:

Nov. 15, 2021, 11:59 p.m. UTC

Place Prize Amount
1st $5,000
2nd $2,000
3rd $1,000
Bonus $2,000

Bonus Award: Top MATLAB User

We're offering a bonus prize of $2,000 to the top contributor using MATLAB. Just make sure to fill out the Software Environment form to be considered for the prize. This is required to be eligible for the bonus prize, though we'd love to hear from you regardless of what environment you're using!

See the MathWorks competition page for more information about complimentary software licenses and learning resources for this challenge.

Note: Bonus recipient can also be a winner of the general prize pool. MathWorks employees are not eligible for prizes.

How to compete

  1. Click the "Join the competition" button in the sidebar to enroll in the competition.
  2. Get familiar with the problem through the about page and problem description. You might also want to reference some of the additional resources from the about page.
  3. Download the data from the data tab.
  4. Create and train your own model. The benchmark blog post, which is written in MATLAB, is a good place to start.
  5. Use your model to generate predictions that match the submission format.
  6. Click “Submit” in the sidebar, and “Make new submission”. You’re in!

Prize generously supplied by our friends at MathWorks.