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Aggressive lane change and cornering detection in the connected vehicle environment

BackgroundSoon, vehicles equipped with Cooperative Vehicle Intelligent Transport Systems (C-ITS) will be on Australian roads. C-ITS will enable several safety functions (use cases) which are expected to improve road safety. After-market products C-ITS based has the advantage of being a feasible solution for existing vehicles with outdated technologies, which might be the origin of the road safety problems. Moreover, C-ITS data enables real-time evaluation of driver behaviour which can be used in several applications such as driver assistance and early …

Study level
Vacation research experience scheme
Faculty
Faculty of Science
School
School of Computer Science

Group activity classification in sports with machine learning

Within a team sports environment, coordinated behaviours between teammates are crucial to success. To accurately detect and correctly classify group behaviours, the actions of the individuals must be considered. Within a sporting environment the problem is further complicated by the presence of an opposing team, who are seeking to counter and disrupt the other team.While there is a substantial volume of research concerning activity detection and related tasks such as segmentation and anticipation from video footage, these tasks are less …

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

Path prediction in sports with machine learning

The ability to predict the future movement of an agent has many applications, from anticipating behaviours within an autonomous driving environment to detecting abnormal behaviours in a security setting. Within sports, being able to predict paths has applications for match analysis, strategy planning, and automated broadcasting.Path prediction is typically performed at an individual level, predicting the future path of each person (or the ball) in turn. While such predictions may consider the locations and past movements of other players (both …

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

Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it is 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 …

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

When does computer vision fail?

Computer vision models predict where objects are in an image, and what those objects are. These vision models give robots the ability to perceive their environment and choose safe and smart actions based on this perception.Computer vision models can fail silently when exposed to unexpected or difficult environments - e.g. changes in camera viewpoints, changes in lighting, or when seeing new objects that haven't been seen before. This raises concerns about the safety of using vision models in the real …

Study level
Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

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
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics

Data reasoning to extend domain knowledge in deep learning

A wide variety of companies now use personalized prediction models to improve customer satisfaction, for example, detecting cancer relapses, Detecting Attacks in Networks (e.g., SDN) or understanding Customer Online Shopping Behaviour. However, the dramatic increase in size and complexity of newly generated data from various sources is creating a number of challenges for domain experts to make personalized prediction.For example, early detection of cancer can drastically improve the chance and successful treatment. Recently, supervised deep learning has brought breakthroughs in …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Computer Science
Research centre(s)
Centre for Data Science

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