Research ThemeSensor Signal ProcessingAimThis project consists of developing single and multi-object detection (and ultimately tracking) algorithms from neuromorphic camera data.ObjectivesIdentification of tractable (front-end) network architectures able to capture spatio-temporal patterns in spiking data Discrimination of spatio-temporal signatures of objects of interest from dynamic clutter. Efficient fusion of neuromorphic and frame-based imaging sensors for real time tracking of fast-moving objects.DescriptionNeuromorphic cameras have enabled low-cost and fast imaging by only recording asynchronously intensity changes. However, efficient processing of the massive streams of polarised events remains a challenge for real-time computer vision tasks such as object detection. Moreover, platform motion also generates dynamic clutter events that can impede object detection and tracking. In this project, we will investigate artificial architectures trained to encode event streams into low temporal resolution frames while also capturing high-resolution spatio-temporal features (e.g., periodicity). Such features can then be used to improve detection and classification performance (e.g., for UAV detection) or platform motion estimation (for enhanced simultaneous localisation and mapping). Finally we will investigate how neuromorphic cameras, operating at different spatial and temporal resolutions can be fused with traditional frame-based cameras (multimodal fusion). Closing date: Sat, 31/01/2026 - 12:00 Apply now Principal Supervisor Prof. Yoann Altmann Assistant Supervisor Prof. Mike Davies Eligibility Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants. Funding Home rate fees and stipend are available for this position.
