Visual tracking with environmental constraints. By incorporating high level scene understanding into visual tracking, this project will improve the capacity to monitor and analyse complex patterns of activity in video. This has many applications in public safety and security, but the project will demonstrate it on the challenging task of tracking players during an Australian Football League (AFL) game to gather statistics on their performance.
Advancing Human–robot Interaction with Augmented Reality. This research aims to advance emerging human-robot interaction (HRI) methods, creating novel and innovative, human-in-the-loop communication, collaboration, and teaching methods. The project expects to support the creation of new applications for the growing wave of assistive robotic platforms emerging in the market and de-risk the integration of collaborative robotics into industrial production. Expected outcomes include methods and tool ....Advancing Human–robot Interaction with Augmented Reality. This research aims to advance emerging human-robot interaction (HRI) methods, creating novel and innovative, human-in-the-loop communication, collaboration, and teaching methods. The project expects to support the creation of new applications for the growing wave of assistive robotic platforms emerging in the market and de-risk the integration of collaborative robotics into industrial production. Expected outcomes include methods and tools developed to allow smart leveraging of the different capacities of humans and robots. This should provide significant benefits allowing manufacturers to capitalize on the high skill level of Australian workers and bring more complex high-value manufactured products to market. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101778
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Building change detection and map update using multispectral imagery and height data. This project will produce an effective building change detection procedure and a digital building map. Automatic building detection assists in taking possible precautions during natural disasters, whilst automatic building change detection facilitates an effective and efficient management of affected areas during and after the calamity.
Robust and scalable change detection in geo-spatial data. A flood of data in the form of text, images and video emanate from a proliferation of sensors. These data are collected but rarely analysed, rendering it meaningless. This project aims to develop new software and techniques to detect changes over time in large scale geographically referenced data (for example photomaps) for use across numerous domains.
Discovery Early Career Researcher Award - Grant ID: DE210101624
Funder
Australian Research Council
Funding Amount
$410,775.00
Summary
Causal Discovery from Unstructured Data. This Project aims to enable machines to discover causal relations from various kinds of unstructured data, such as images, text files, and sensor data. The project expects to promote causal revolution of data-centric intelligence and science – construct machines that can communicate in the language of cause and effect and answer ‘why’ questions by inferring from unstructured data. Expected outcomes of this project include theoretical foundations for causa ....Causal Discovery from Unstructured Data. This Project aims to enable machines to discover causal relations from various kinds of unstructured data, such as images, text files, and sensor data. The project expects to promote causal revolution of data-centric intelligence and science – construct machines that can communicate in the language of cause and effect and answer ‘why’ questions by inferring from unstructured data. Expected outcomes of this project include theoretical foundations for causal discovery from unstructured data and practical algorithms that drive intelligent machines to make rational decisions in real-world scenarios. This should benefit society and the economy nationally and internationally through the applications of artificial intelligence and data science. Read moreRead less
Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emer ....Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emergency services. This new capability enhances utilisation of security resources to prevent injury and fatalities in evacuation scenarios, applicable to existing venues and influencing the development of new facilities around the country. The project delivers researcher training, global clientele for local technology and a platform for local industry growth.Read moreRead less