This semester I proposed the topic of Species Classification in Thermal Imaging Videos, and this was selected to be one of the group projects. The objective of this project was to improve the machine learning models used to identify species in thermal imaging videos, which can be used to automate monitoring of remote sites, and perhaps ultimately contribute to the development of smart traps which can improve pest and predator control.
We were fortunate to have The Cacophony Project provide us with a data set of videos they have collected from several locations in New Zealand.
Significant credit must be given to Hamish Duncanson. Between us, we contributed the vast majority of the research and writing for this paper.
I have embedded our final report in the page below. Alternatively, you can download a pdf copy of this paper here.
We examine a labelled dataset of thermal imaging recordings of animals in New Zealand bush, for the purpose of developing models which can automate the labelling of species. After approaching the problem with generic video classification models, we propose two novel contributions:
Experiments on the provided dataset support the efficacy of these contributions. We present a Inflated 3-D Convolutional Network (I3D), which combined with these techniques, can correctly label the species of over 90% of the thermal imaging recordings.