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.
Previously we used a static camera so the tracking part of the problem was quite easy as only the animals moved, whereas background objects (such as trees) stayed relatively still. This is obviously harder when the camera is moving. Below shows some examples of the new tracker working with the moving camera.
In the video below the one on the left is the raw thermal image and on the right is the tracked image that has removed the background. The classifier that is trying to guess the object has not been trained on humans or radio controlled cars so clearly its guessing incorrectly (the bloke at the end does have some stoat-like tendencies though...).
Video 1: Tracker working on a moving camera
This is another small step along the path to a system that can lure identify and eliminate all predators.