Semantic change detection through large-scale learning. This project aims to develop technologies which understand the content of images before higher-level analysis is performed. This approach is intended to allow more accurate and reliable decisions to be made using automated image analysis than has previously been possible. The project will particularly investigate the detection of change in the contents of an image.
Automatic real-time detection of infiltrated objects for security of airports and train stations. Infiltrated objects represent a very high security threat in critical areas such as airports and train stations. In order to neutralise such a threat, this project will develop new automatic technologies capable of detecting infiltrated objects in sensitive areas in real time, analysing the movements of their original carriers in the nearby areas, and raising attention accordingly. The technologies ....Automatic real-time detection of infiltrated objects for security of airports and train stations. Infiltrated objects represent a very high security threat in critical areas such as airports and train stations. In order to neutralise such a threat, this project will develop new automatic technologies capable of detecting infiltrated objects in sensitive areas in real time, analysing the movements of their original carriers in the nearby areas, and raising attention accordingly. The technologies will be based on the automatic analysis of camera videos made by computers without the need for assessing or storing the identities of common passers-by. The potential of application is huge extending beyond airports and train stations to any public areas.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
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.
Towards in-vehicle situation awareness using visual and audio sensors. This project aims to characterise driver awareness, activity and interactions with other vehicle occupants using visual and audio cues from internally mounted sensors. Road accidents cost Australia an estimated $30 billion per year and tragic loss of thousands of lives, yet the vast majority of severe vehicle crashes are linked to driver fatigue or distraction. The expected project outcomes include advanced artificial intelli ....Towards in-vehicle situation awareness using visual and audio sensors. This project aims to characterise driver awareness, activity and interactions with other vehicle occupants using visual and audio cues from internally mounted sensors. Road accidents cost Australia an estimated $30 billion per year and tragic loss of thousands of lives, yet the vast majority of severe vehicle crashes are linked to driver fatigue or distraction. The expected project outcomes include advanced artificial intelligence to infer and predict dangerous driver and passenger behaviour. This has the potential to significantly benefit society by advancing autonomous driving capabilities and reducing driver-induced accidents and fatalities, ensuring that every driver, passenger and pedestrian arrives home safely at the end of each day.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
Fusion of digital microscopy and plain text reports for automated analysis. The project aims to develop advanced computer-aided analytics systems with the goal to improve the workflow and automation in the pathology industry. Improvements will be achieved by fusing information from both digital images and plain text medical reports. In collaboration with a partner organisation, the project team will field trial the new analytics systems against traditional pathology tests to evaluate both effica ....Fusion of digital microscopy and plain text reports for automated analysis. The project aims to develop advanced computer-aided analytics systems with the goal to improve the workflow and automation in the pathology industry. Improvements will be achieved by fusing information from both digital images and plain text medical reports. In collaboration with a partner organisation, the project team will field trial the new analytics systems against traditional pathology tests to evaluate both efficacy and reliability. In addition, the project is also aimed to construct a large digital slide databank which will aid training and education. The expected outcome of the project is to perform existing tasks cheaper and more efficiently. Read moreRead less
Scalable classification for massive datasets: randomised algorithms. The project will develop multi-class classification technologies capable of distinguishing between tens of thousands of classes, which are trained and applied to massive datasets. The technology will be applied in the field of microbiology, bringing high quality digital imaging and machine learning to this important area.
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.
Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project aims to develop new techniques for identification of roadside obje ....Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project aims to develop new techniques for identification of roadside objects so that the data can be automatically analysed allowing the estimation of fire risk factors. The final outcome intends to be techniques for segmentation and classification of roadside objects and estimation of fire risk factors.Read moreRead less