Trusted Autonomous Systems
As the application of autonomous systems becomes more widespread across industries including health, education and the finance, the emphasis on a cooperative relationship between human and machine becomes even more significant.
To develop a trusted autonomous system to function reliably in often complex environments is a key challenge requiring reliable research and innovation.
At AIML, we are developing trusted autonomous systems is a key research priority. We have successfully developed autonomous systems that are aware of the uncertain environment and can perform complex tasks. Our theoretical and practical research is trending in developing systems that are capable of providing transparent and explainable decisions, intelligently controlling autonomous vehicles, asking questions when uncertain about decisions or the surrounding environment and understanding their interactions with the world and applying reasoning to their surrounds.
Surveillance and Tracking
AIML carries out surveillance research in the areas of camera calibration, visual tracking, novel-view synthesis, automatic visual surveillance and simultaneous localisation and mapping.
Computer vision enables machines to constantly scan an environment for pre-learned patterns. In urban environments, computer vision can identify maintenance needs for local councils, or identify illegal developments. In mining, computer vision surveillance can track assets and monitor mine spoil changes over time. In defence, the ongoing surveillance of potential threats can be automated using a variety of computer vision-enabled platforms