A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Field and quasi-field phenotyping for the quantitative characterisation of wheat yield under stress. The project aims to develop state-of-the-art monitoring and profiling capabilities for the quantitative assessment of plant growth performance in field and quasi-field environments under the abiotic stress conditions of drought and nutrient deficiency. This project involves the design and use of high resolution but low budget imaging stations to capture the growth of cereal plants in competitive ....Field and quasi-field phenotyping for the quantitative characterisation of wheat yield under stress. The project aims to develop state-of-the-art monitoring and profiling capabilities for the quantitative assessment of plant growth performance in field and quasi-field environments under the abiotic stress conditions of drought and nutrient deficiency. This project involves the design and use of high resolution but low budget imaging stations to capture the growth of cereal plants in competitive environments. Novel computer vision and image processing techniques will be applied to the image data to quantitatively characterise the success of genetic varieties to tolerate abiotic stress environments under actual field conditions.Read moreRead less
Scalable Robust Video Surveillance over Constrained Networks. Real-time monitoring of large numbers of people is becoming increasingly important for applications such as efficient service delivery and security against both common crime and terrorism. The use of human operators for such tasks is infeasible due to the large amount of data collected. Existing autonomous video surveillance systems are prone to high numbers of false alarms and often require expensive hardware. This proposal seeks ....Scalable Robust Video Surveillance over Constrained Networks. Real-time monitoring of large numbers of people is becoming increasingly important for applications such as efficient service delivery and security against both common crime and terrorism. The use of human operators for such tasks is infeasible due to the large amount of data collected. Existing autonomous video surveillance systems are prone to high numbers of false alarms and often require expensive hardware. This proposal seeks to address both difficulties by using rigorous statistical signal processing methods to optimally fuse information from a network of low-cost cameras.Read moreRead less
Automation of species recognition and size measurement of fish from underwater stereo-video imagery. The project aims to develop algorithms to automate the processing of stereo-video images recorded to count and measure the size of fish. This will improve husbandry and monitoring for finfish aquaculture at reduced costs, create technology export for industry partners, and develop cost effective, non-destructive finfish sampling tools for marine agencies.
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.
Space-based space surveillance with robust computer vision algorithms. Space-based space surveillance with robust computer vision algorithms. This project aims to develop computer vision algorithms to detect man-made objects in space. These algorithms function on nanosatellite platforms, enabling space-based space surveillance. This technology is expected to provide always-on monitoring of the Earth's orbit to enhance existing defence infrastructure and protect vital space assets, including comm ....Space-based space surveillance with robust computer vision algorithms. Space-based space surveillance with robust computer vision algorithms. This project aims to develop computer vision algorithms to detect man-made objects in space. These algorithms function on nanosatellite platforms, enabling space-based space surveillance. This technology is expected to provide always-on monitoring of the Earth's orbit to enhance existing defence infrastructure and protect vital space assets, including communications and navigational satellites, in Earth’s orbit from collisions and covert sabotage. Increased space use by government and civilian agencies opens up opportunities for the space industry. This project is expected to develop Australia’s space surveillance capabilities, protect space assets and capture a growing market.Read moreRead less
Crowd tracking and visual analytics for rapidly deployable imaging devices. Crowd tracking and visual analytics for rapidly deployable imaging devices. This project aims to develop visual analytics technology that adds machine intelligence to a rapidly deployable time-lapse imaging platform. Such devices can operate on solar and wind power, and be remotely programmed (via a cellular network) to take photos and send them to a server at given times. This project, which focuses on monitoring crowds ....Crowd tracking and visual analytics for rapidly deployable imaging devices. Crowd tracking and visual analytics for rapidly deployable imaging devices. This project aims to develop visual analytics technology that adds machine intelligence to a rapidly deployable time-lapse imaging platform. Such devices can operate on solar and wind power, and be remotely programmed (via a cellular network) to take photos and send them to a server at given times. This project, which focuses on monitoring crowds of objects of interest, is expected to introduce “smart” imaging platforms that could be triggered and shoot high-quality photographs when “events of interest” occur. This project could make Australia both a world leader in video analytics and secure through on-line threat detection, and improve traffic control and agriculture.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