Detecting wildlife in uncontrolled outdoor video using convolutional neural networks

Abstract

This paper explores the use of Convolutional Neural Networks (CNNs) to detect Interior Least Tern in uncontrolled outdoor videos for the Wildlife@Home project. To be able to use CNNs on this video, this work developed strategies to bridge the gap between video collected by wildlife biologists and the methodlogies common for training and testing CNNs by utilizing a striding methodology to extract positive and negative training examples of a fixed size. Then in order to efficiently run trained CNNs over full videos, software was developed using OpenCL which was capable of utilizing multiple GPUs and other OpenCL capable compute devices concurrently. It was also shown that an already trained CNN can be further refined by training it further on new imagery, without having to retrain the whole network from scratch, saving significant time. Further, while the CNNs trained were only for detection of Interior Least Terns, they show promise for actually detecting behavior, as obvious peaks resulted for periods of video when a tern was in flight. To the authors' knowledge, this is the first attempt to utilize CNNs for the task of detecting wildlife in uncontrolled outdoor video.