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Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it's highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:detecting target animalscounting herd animalsclassifying land useassessing environment …

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Gesture-based control of underwater helper-bots

Underwater robotic systems have been in use for several decades. In recent years, various groups have been adding manipulators and other payloads to increase their utility. However, their role has primarily been monitoring and mapping the oceans without human interaction.The next frontier is to have human divers and robotic system collaborate safely and productively in the same space to jointly complete complex tasks. This will involve the robotic system directly understanding and interacting with the diver in a non-verbal manner. …

Study level
PhD, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Augmented reality (AR) applications for robotic scene understanding

Augmented reality (AR), or mixed reality, has become a mature technology with many possible practical applications in manufacturing, retail, navigation and entertainment.We're interested in using AR to support human-robot interaction. In this project, you'll investigate how a human can use AR to better understand how a robot perceives the world and to understand the robot's intentions.

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Deep learning for robotics in open-world conditions

To fully integrate deep learning into robotics, it's important that deep learning systems can reliably estimate the uncertainty in their predictions. This allows robots to treat a deep neural network like any other sensor and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems typically return scores from their softmax layers that are proportional to the system’s confidence. They are not calibrated probabilities and …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

New technology and the law

Computer vision has developed to a point where machines using artificial intelligence are better and faster than humans at performing many vision-related tasks. For example, we are now often processed through customs based solely on face recognition software. Add to this the fact that the average Australian is photographed on CCTV cameras around 75 times per day. Commercial applications of face recognition technology include Microsoft's Face Application Programming Interface that can be used to classify face images based on gender, …

Study level
PhD, Master of Philosophy
Faculty
null
School
null
Research centre(s)
null
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