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.

The Cacophony Project's Menno Finlay-Smits recently recorded an episode with Dave Lane for the Access Granted podcast. It's a great introduction to the what the project is all about and where we're heading.

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.

To ensure that we were seeing all predators in a given area, we developed a heat camera specifically targeted at small nocturnal mammals. Initial results indicate that this picks up 2-3 times as many predators as off the shelf trail cameras due to greater sensitivity and longer range.

With the generous help of Willowbank Wildlife Reserve we have been collecting thermal video footage of kiwis to help train our machine learning based animal classifier.

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.

Our recordings include many false positives - where a non-animal object triggered our camera's motion detector. We also have many, many recordings of birds - particularly at dawn and dusk. Filtering through all the false positives and bird footage is very time consuming.

This is just a quick peek at some very encouraging results showing that Artificial Intelligence can successfully work out what it is seeing from the video. We will put up more discussion and details next year.

Hi everyone, my name is Matthew, I have been brought on to help with the machine learning side of things. I’m very excited to be part of this project.
My job here is to take all the thermal footage we have been recording and identify the animals in it.

This post will describe the work I’ve done and basically what The Cacophony Index 1.0 is.
The purpose of the index is to tie together the data we’ve started to collect and use it to help understand how they are going. It can allow us to understand how well different predator control techniques are working and in turn improve them.

WWF New Zealand is currently taking applications for the Conservation Innovation Awards 2017. They're looking for big ideas for tackling conservation issues in New Zealand and the Cacophony Project has entered the Predator Free New Zealand 2050 category.

A major focus for us at the moment is the collection of a wide variety of thermal video footage featuring invasive predators (and non-target species too). These videos will be used to train our machine learning model to correctly identify the various invasive predators that we're interested in. There's been plenty of progress on several fronts.

We now have our FLIR Lepton 3 thermal cameras working well enough to start getting more out in the field. For the geeks out there, it turns out that a major source of unreliability was that the Raspberry Pi's SPI clock is tied to the CPU clock. When CPU "turbo mode" kicks in the SPI clock speed also increases, generating an SPI clock rate that is incompatible with the camera.

As reported in the last update, we've been having some trouble getting a reliable video feed out of the new thermal cameras we're using. We've made a significant breakthrough on the software side around packet handling and sync recovery which has improved performance significantly.

I've been reviewing the current state of our hardware and software efforts, meeting various advisors and contributors, and getting a grasp of our immediate and longer term goals. It's been fantastic to meet everyone and I'm really excited to be onboard. There's a real energy around the project.

My name is Menno Finlay-Smits and I start today as the Lead Developer and Project Manager for the Cacophony Project. We've got a real chance at solving the problem of invasive predators in New Zealand and I'm really excited to be part of the effort.