Automatic animal detection using unmanned aerial vehicles in natural environments

In the past decade, small unmanned aerial vehicles have become increasingly popular for remote sensing applications because of their low costs, and easy and fast deployment. Together with the development of light-weight imaging sensors, these UAVs have become valuable tools for monitoring and analyzing large areas from above. In the Netherlands, the development of agricultural and livestock sectors plays an important role. The use of an unmanned aerial vehicle allows visualization of the crowns of cultures and monitoring livestock in a large area, which increases the ability of interpretation and diagnosis from the data collected, thus contributing to increase agricultural productivity. While quickly collecting large amounts of imagery data from the UAVs is becoming more straightforward, analyzing these data is still mostly a laborious demanding manual task. Major issues for object detection are annotating large amount of training data and finding correct feature descriptors and classifiers. A general framework for detecting objects in natural environments using UAVs is developed in this research. The object detection method can be bootstrapped with minimal expert annotation of data that are collected using an affordable commercial UAV. Different machine learning techniques are analyzed to find which maximize the object detection success. The resulting object detector can be trained using active learning techniques to reduce manual labeling effort, and allows for harvesting detected objects to increase the output performance.