I sense, therefore I help: Towards homes that sense and support the aged and infirm. The overall goal is to produce technologies that will enable the home to be a ?intelligent, caring advisor? and provide operational effectiveness for people with decreasing functional capacity (eg aged). It can monitor and support activities of varying complexity without compromising a normal lifestyle, enabling people of varying abilities to be independent, whilst being cared for by their homes. This caring wi ....I sense, therefore I help: Towards homes that sense and support the aged and infirm. The overall goal is to produce technologies that will enable the home to be a ?intelligent, caring advisor? and provide operational effectiveness for people with decreasing functional capacity (eg aged). It can monitor and support activities of varying complexity without compromising a normal lifestyle, enabling people of varying abilities to be independent, whilst being cared for by their homes. This caring will range from advice systems for detecting hazardous situations and alerting the user, through to detecting subtle deviations from complex, normal activities over significant periods of time (such as caused by the onset of an illness).Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE100100235
Funder
Australian Research Council
Funding Amount
$280,000.00
Summary
Accelerating Australia's large scale video surveillance research programmes. The research to be conducted using this infrastructure will bring immense benefits to Australia in terms of increased levels of public safety and in the protection of critical facilities from terrorism and other crimes, by developing better surveillance systems. This will provide both increases in measurable research outputs and opportunities for Australian business to commercialise these systems. The infrastructure wil ....Accelerating Australia's large scale video surveillance research programmes. The research to be conducted using this infrastructure will bring immense benefits to Australia in terms of increased levels of public safety and in the protection of critical facilities from terrorism and other crimes, by developing better surveillance systems. This will provide both increases in measurable research outputs and opportunities for Australian business to commercialise these systems. The infrastructure will accelerate the pace of surveillance research and development in Australia, enhancing the competitiveness of both Australia's researchers and the businesses that will commercialise these researchers' discoveries.Read moreRead less
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.
Tensor and Hypergraph Methods in Fitting Visual Data. This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.
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
Taming media for the masses: Computational frameworks for intelligent digital media capture, management, and sharing. The core issues tackled in this project are learning, recognition and application of semantics in multimedia data and the context of its creation and use - a foundational issue in pattern recognition with many applications. The project is part of the Institute for Multi-sensor Processing and Content Analysis whose aim is to tackle technical issues in large scale pattern recogniti ....Taming media for the masses: Computational frameworks for intelligent digital media capture, management, and sharing. The core issues tackled in this project are learning, recognition and application of semantics in multimedia data and the context of its creation and use - a foundational issue in pattern recognition with many applications. The project is part of the Institute for Multi-sensor Processing and Content Analysis whose aim is to tackle technical issues in large scale pattern recognition. By developing scalable and robust techniques to extract information from large scale multi-modal data, the applications include large scale surveillance systems from multi-modal data (e.g. airport security, smart homes for the aged), context-aware devices, and the next generation of media creation and repurposing tools - a fast-growing sector of the economy.Read moreRead less
Integration of Spatiotemporal Video Data for Realtime Smart Proactive Surveillance. This project will have a great impact on the national security by helping the law enforcement agencies to stop crime before it happens. It will automatically detect and tag criminal activities in surveillance videos. It will detect, authenticate, track and profile individuals in sensitive installations. At airports, it will match faces to electronic images embedded in passports. The system will use existing surve ....Integration of Spatiotemporal Video Data for Realtime Smart Proactive Surveillance. This project will have a great impact on the national security by helping the law enforcement agencies to stop crime before it happens. It will automatically detect and tag criminal activities in surveillance videos. It will detect, authenticate, track and profile individuals in sensitive installations. At airports, it will match faces to electronic images embedded in passports. The system will use existing surveillance infrastructures for locating lost people and will also ensure privacy protection of public. On the commercial side, this project can recognize old customers for better and customized services. It can count the number of people present in each floor of a building for rescue operations and for designing future buildings.Read moreRead less
Bridging the semantic gap for building effective content management systems: Computational media aesthetics. This project focuses on video abstraction and aims to bridge the semantic gap between the simplicity of available visual features and the richness of user descriptions. We examine how visual and aural techniques are brought together to influence the engagement of audience in a story portrayal. The major outcome will be a computational framework for extracting the semantics associated wi ....Bridging the semantic gap for building effective content management systems: Computational media aesthetics. This project focuses on video abstraction and aims to bridge the semantic gap between the simplicity of available visual features and the richness of user descriptions. We examine how visual and aural techniques are brought together to influence the engagement of audience in a story portrayal. The major outcome will be a computational framework for extracting the semantics associated with audiovisual elements in television/film, and scalable software tools that can rapidly and consistently analyse media along various aesthetic dimensions. It will allow for high-level annotation of media and the building of more effective content management systems with enhanced user querying capabilities.Read moreRead less
Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learni ....Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learning architectures that are inherently robust. The outcomes of this project will increase the security and reliability of computer vision by detecting, reporting and nullifying such attacks and will benefit the general public and industry on many fronts.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.