Case study Lion Guards

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Lion preservation with computer vision

A computer vision approach proved to be successful in animal tracking.

Headquarters

Africa

Organization type

NGO

Founded

2017

Helping wild animals conservation with machine learning.

The Lion Identification Network of Collaborators - Lion Guards - monitors lions to help with their preservation across African territory. This community of conservationists and researchers in conjunction with IEF R&D created an open-source platform to track Panthera Leo in order to maintain the genetic viability of the species and create better policy decisions to protect the last remaining lions.

465

individual lions registered

Challenge

Conservationists needed to automate the process of identification of over 400 lions. To accurately monitor the population and better understand the connectivity between them, researchers in the past used to manually track lion's movements by comparing hundreds of pictures or utilizing GPS collars.

Solution

A computer vision system with a pattern recognition software now serves as an automated, non-invasive solution for identifying and monitoring lions. By recognizing face and whisker patterns in lion images conservationists can collectively locate the animals by sharing their images information in a system that classifies and identifies the population.

approach

Discover our approach

Develop a computer vision automated solution and pattern recognition software in 3 stages:

Lion feature object detection

Using a dataset of lion images that are tagged with bounding boxes around features, such as head, eyes and nose, the algorithm identifies the feature classes in new photos.

The result of our partnership shows the flexibility and an openness of Tryolabs to out of the box thinking essential to developing not just a powerful algorithm but one that can work on all levels of the conservationist work practice.

It has been a great working experience and our team looks forward to working with Tryolabs in the future on this unique conservation challenge.