Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcom ....Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcomes include the ability to ingest multiple video feeds into a dense and dynamic 3D reconstruction for knowledge representation and discovery, and analysis of events and behaviour through new spatio-temporal analytic approaches. This will offer significant benefits for video forensic analysis, policing, and emergency response.Read moreRead less
Privacy-aware Smart Access Control for Internet-of-Things on Blockchain. This project aims to address privacy and trust issues in Internet-of-Things (IoT) access control mechanism of smart critical infrastructure. This project expects to generate new knowledge in the area of IoT access control by leveraging privacy-preserving techniques, blockchain, and machine learning. Expected outcomes of this project include enhanced capability to build improved techniques for privacy aware tamperproof IoT a ....Privacy-aware Smart Access Control for Internet-of-Things on Blockchain. This project aims to address privacy and trust issues in Internet-of-Things (IoT) access control mechanism of smart critical infrastructure. This project expects to generate new knowledge in the area of IoT access control by leveraging privacy-preserving techniques, blockchain, and machine learning. Expected outcomes of this project include enhanced capability to build improved techniques for privacy aware tamperproof IoT access control with machine learning based anomaly detection. This should provide significant benefits, such as preventing cyber threats on security and privacy of IoT and improving trust in IoT-enabled smart critical infrastructure of Australia.Read moreRead less