David Blake is a semi-retired property investor who now likes to spend his time trapping pests and planting native trees. From time to time he helps out by volunteering at The Cacophony Project doing some filming and occasionally contributing to parts of the software.
The video in this link shows a visual representation of the contributions to the software components of the project over time. The visualisation is based on source code changes pushed to Github and shows the areas of the software being worked on and the people involved.
The Cacophony Project has very long-term goals to enable us to eliminate 100% of predators. It’s easy to look at what we are doing and assume that this will never work in the depths of the remote bush. While we acknowledge that there are many steps before we achieve that capability, the tools developed while getting to that end goal will be useful for other important parts of the problem.
You have to love the way journalists make a headline. They get most of the details of the actual project pretty right though
Below is a talk from the recent New Zealand AI conference. It gives an up-to-date summary of the project with particular reference to how we are using Machine Learning.
As with all projects when the fun development parts are proven there is refinement required to make the devices more robust and usable. Below are a few photos of the next iteration of our hardware that makes the product more reliable and flexible.
We have been invited to present our work at New Zealand's AI conference. There is currently huge interest in exploring applications for AI around the world and in New Zealand. The Cacophony Project is a great example of how this field can be used to tackle a very New Zealand specific problem.
The first step required to become 100% predator-free is to know for sure what type of predators are out there. The most common methods used to do this are tracking tunnels and chew cards. These tools require significant manual work and miss a lot of predators.
The Machine Learning (artificial intelligence) identification has two main parts. A tracker locates any objects of interest and separates them from the background. These tracks are then passed to the classifier which analyses them to identify what sort of animal they contain.
In previous blog post we have talked about the advantages of having a camera that can track animals. It will obviously allow better identification but also the ability to eliminate with something like a poison paintball.