Honours and Minor Thesis projects

The ability to forecast human trajectory and/or body motion (i.e. pose dynamics and trajectory) from camera or other visual sensors is an essential component for many real-world applications, including robotics, healthcare, detection of perilous behavioural patterns in surveillance systems.

Human behaviour understanding in videos is a crucial task in autonomous driving cars, robot navigation and surveillance systems. In a real scene comprising of several actors, each human is performing one or more individual actions. Moreover, they generally form several social groups with potentially different social connections, e.g. contribution toward a common activity or goal.

3D localisation, reconstruction and mapping of the objects and human body in dynamic environments are important steps towards high-level 3D scene understanding, which has many applications in autonomous driving, robotics interaction and navigation. This project focuses on creating the scene representation in 3D which gives a complete scene understanding i.e pose, shape and size of different scene elements (humans and objects) and their spatio-temporal relationship.

In this project, the goal is to develop a new method (using computer vision and machine learning techniques) for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout and navigating as an active observer in which the predictions inform actions.

To operate, interact and navigate safely in dynamic human environments, an autonomous agent, e.g. a mobile social robot, must be equipped with a reliable perception system, which is not only able to understand the static environment around it, but also perceive and predict intricate human behaviours in this environment while considering their physical and social decorum and interactions.

This project involves development of a machine learning based tool to ‘passively’ learn and measure organisational culture in real time using existing organisational data and propose positive cultural interventions.

The Optimisation group is looking for multiple students to contribute to our world leading research. Our interests range from practical to theoretical. So whether you are interested in path finding for AI in games, solving a complex scheduling problem, designing new algorithms, or working on our specialised modelling language, we will have a project that is of interest to you!

Despite the rapid progress made recently, deep learning (DL) approaches are data-hungry. To achieve their optimum performance, a significantly large amount of labeled data is required. Very often, unlabelled data is abundant but acquiring their labels is costly and difficult. Many domains require a specialist to annotate the data samples, for instance, the medical domain. Data dependency has become one of the limiting factors to applying deep learning in many real-world scenarios.

In multi-label classifications, a data sample is associated with more than one active label, which is a more challenging task than conventional single-label classifications. This project will focus on eXtreme Multi-Label (XML) classifications for text data (i.e., documents), where the label set can be extremely large, e.g., more than 10,000. For example, the input texts can be the item descriptions of an e-commerce website (e.g., Amazon) and one needs to classify them into a large set of item categories.

Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic speech recognition, and autonomous driving, etc. However, due to the intrinsic vulnerability and the lack of rigorous verification, DL systems suffer from quality and security issues, such as the Alexa/Siri manipulation and the autonomous car accidents, which are introduced from both the development and deployment stages.