QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 13 matching student topics
Displaying 1–12 of 13 results
Sport AI
Videos of sport activities are widely available at large scales. AI and its sub-fields, especially computer vision and machine learning, have a great potential to analyse, understand and extract useful information from these videos.This project aims at using AI and its subfields in computer vision and machine learning to develop techniques for analysing sport videos to extract intelligence for players and coaches.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Drone and satellite Artificial Intelligence
Satellite and drone/UAV data has a great potential to provide large-scale analytics for many domain applications. However, the wide range of data of diverse nature (e.g., optical vs. SAR, high-resolution vs. wide-coverage, mono- vs. hyper-spectral, 2-D vs. 3-D) also poses significant challenges for analytics.Deep learning holds great promise to deal with these tasks. While the number of research in this area is increasing, there still exists challenges such as co-learning of multimodal data, limited data annotation, and uncertainty in the …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Conversational agents that can see
The development of conversational agents, whether as smart home devices, or embedded in mobile devices or social robots, has started in the world of chatbots, with only text available, and then started to build audio features, and finally considering context through sensors and cloud knowledge, as well as offering images in response to a query.However, little attention has been paid to other conversational modalities, such as showing, pointing, or gesturing. The reliance on these is exacerbated in conversation with people …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
Artificial Intelligence for collaborative and intelligent user interfaces
This project seeks to leverage recent advances in machine vision and natural language processing algorithms to support the design and development of knowledge-driven applications that support communication and collaborations with their users.One particular area where this will be investigated is in workplaces for supported employment, that is employment opportunities for people with intellectual disability. One of the questions to address is how machines could respond to what a user shows them in order to assist with decision making in a …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
Understanding responsible deployment of computer vision for urban planning
Advances in artificial intelligence (AI) offer urban planning practice many novel prospects. By the responsive use of AI, planners can effectively analyse data, improve processes, increase efficiency, and prioritise human-centric aspects of planning to develop sustainable cities. Computer vision is one of the key areas where responsible AI is applied in urban planning to revolutionise the analysis and interpretation of visual data, like images and videos captured in cities to aid decision and plan making processes. While the potential impacts …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Physics-informed machine learning
Recent advances in computer vision have demonstrated superhuman performance on a variety of visual tasks including image classification, object detection, human pose estimation and human analysis. However, current approaches for achieving these results center around models that purely learn from large-scale datasets with highly complex neural network architectures. Despite the impressive performance, pure data-driven models usually lack robustness, interpretability, and adherence to physical constraints or commonsense reasoning.As in the real world, the visual world of computer vision is governed by …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Machine learning for understanding and predicting behaviour
Understanding behaviour and predicting events is a core machine learning task, and has many applications in areas including computer vision (to detect or prediction actions in video) and signal processing (to detect events in medical signals).While a large body of research exists exploring these tasks, a number of common challenges persist including:capturing variations in how behaviours or events appear across different subjects, such that predictions can be accurately made for previously unseen subjectsmodelling and incorporating long-term relationships, such as previously …
- Study level
- PhD, Master of Philosophy
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Investigating the application of sustainable AI practices in construction
The construction industry plays a vital role in the global economy and there is a growing interest in utilising artificial intelligence (AI) to improve its productivity and efficiency. Despite the industry's significant contribution to the economy, it has faced challenges such as large cost overruns, extended schedules, and quality concerns. Nevertheless, AI is making significant strides to remove these issues by revolutionising various aspects of the construction industry. This is evident from enhancing project planning and design to improving construction …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Engineering
- School
- School of Architecture and Built Environment
Enhancing 3D visual understanding through multimodal data fusion
The demand for 3D scene understanding through point clouds is rapidly growing in diverse applications, including augmented and virtual reality, autonomous driving, robotics, and environment monitoring. However, the field faces challenges due to limited data availability and predefined categories. Training deep 3D networks effectively for sparse LiDAR point clouds requires significant amounts of annotated data, which is both time-consuming and expensive. Building on the advancements in 2D models that leverage the power of image and language knowledge, our project aims …
- Study level
- PhD, Master of Philosophy, Honours
- School
- School of Electrical Engineering and Robotics
Re-localisation in natural environments
Re-localisation in robotics involves the process of determining a robot's current pose, consisting of its position and orientation. This can either be within a previously mapped and known environment (i.e. prior map) or relative to another robot in a multi-agent setup. Re-localisation is essential for enabling robots to perform tasks such as autonomous monitoring and exploration seamlessly, even when they encounter temporary challenges in precisely tracking their location in GPS-degraded environments. For instance, consider the 'wake-up' problem, where a robot …
- Study level
- PhD
- School
- School of Electrical Engineering and Robotics
Advancing monitoring of diverse grass pollen with computer vision
We're seeking a motivated student to join the multidisciplinary project that brings together computer vision and deep learning field with pollen aerobiology. This is a fully funded PhD program for a three-year period starting in 2024. It's part of the project funded by the Australian Research Council Discovery Program—Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. The successful candidate will be primarily based in the Allergy Research Group at QUT's Kelvin Grove campus.Grass pollen is the main outdoor allergen …
- Study level
- PhD
- School
- School of Biomedical Sciences
- Research centre(s)
- Centre for Robotics
Centre for Immunology and Infection Control
Analysis of professional squash matches
This project concerns computer vision and statistical analysis of performance in professional level matches in the game of squash.The goal is to use computer vision and existing systems to capture and analyse patterns of play, allowing coaches and professional players to develop strategies to improve performance, to counter particular types of play and even to tailor game plans to attack individual opponents.
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Computer Science
- Research centre(s)
- Centre for Data Science
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