Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to deve ....Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.Read moreRead less
Autonomous Functions for Smart Cars. The aim of this project is to develop autonomous functions for smart cars, such as lane departure warning, driver fatigue warning, and automatic lane following. Every year 70,000 people are killed in road accidents, 95% of which can be attributed to driver error. The potential outcomes of this project therefore significant. Many of the theoretical methods required for this project have been developed by our group. However, further theoretical refinements fo ....Autonomous Functions for Smart Cars. The aim of this project is to develop autonomous functions for smart cars, such as lane departure warning, driver fatigue warning, and automatic lane following. Every year 70,000 people are killed in road accidents, 95% of which can be attributed to driver error. The potential outcomes of this project therefore significant. Many of the theoretical methods required for this project have been developed by our group. However, further theoretical refinements followed by experimental verification is necessary. For smart cars to be accepted, the systems must be demonstrated to be reliable and to operate in a wide range of conditions.Read moreRead less
Visual tracking of multiple objects: A stochastic geometrical approach. Reliable real-time visual multiple-object tracking techniques will open up new applications that enhance the quality of life such as driving safety, traffic monitoring, home security, security and surveillance of public facilities. These new applications have huge commercial potentials, and the technology developed from this project will provide the catalyst for vibrant new industries to grow. In addition, the expertise deve ....Visual tracking of multiple objects: A stochastic geometrical approach. Reliable real-time visual multiple-object tracking techniques will open up new applications that enhance the quality of life such as driving safety, traffic monitoring, home security, security and surveillance of public facilities. These new applications have huge commercial potentials, and the technology developed from this project will provide the catalyst for vibrant new industries to grow. In addition, the expertise developed from the project will provide a competitive edge for Australian industries in aerospace, oceanography, robotics, remote sensing, and biomedical engineering. Read moreRead less
A Novel System for Surveillance of Moving Objects. Surveillance of moving objects is critical in numerous applications such as detection and recognition of motor vehicles. It is important for detection to be fast and accurate with low cost. In this project, we aim to implement a surveillance system consisting of an efficient algorithm on a PC network with a camera. Our detection algorithm will be achieved with an advanced and computationally powerful image representation for fast computation. It ....A Novel System for Surveillance of Moving Objects. Surveillance of moving objects is critical in numerous applications such as detection and recognition of motor vehicles. It is important for detection to be fast and accurate with low cost. In this project, we aim to implement a surveillance system consisting of an efficient algorithm on a PC network with a camera. Our detection algorithm will be achieved with an advanced and computationally powerful image representation for fast computation. Its accuracy will be enhanced by adapting a well recognized theory for fast removal of image noise. Our implementation on the PC network will provide a flexible and extensible platform for parallel computing to further reduce detection time while keeping costs low.Read moreRead less
AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing n ....AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.Read moreRead less
Advanced techniques for imaging radar interferometry. The Earth's surface is changing all the time, both slowly and dramatically, due to activities such as groundwater extraction, underground mining and earthquakes. This project will develop advanced, cost-effective and accurate imaging radar techniques that can measure subtle surface changes frequently, in order to safeguard significant infrastructure.
Intelligently Activated Sensor Clusters for E-Commerce Applications. This project will investigate intelligent management of large sensor clusters installed in mechanical structures for use in Electronic Commerce applications. Finding algorithms for optimised placement of remotely controlled power supply and the communication unit in each sensor cluster is the first aim. Development of a sensor management algorithm that process user inputs submitted through the Internet and that exploits past se ....Intelligently Activated Sensor Clusters for E-Commerce Applications. This project will investigate intelligent management of large sensor clusters installed in mechanical structures for use in Electronic Commerce applications. Finding algorithms for optimised placement of remotely controlled power supply and the communication unit in each sensor cluster is the first aim. Development of a sensor management algorithm that process user inputs submitted through the Internet and that exploits past sensor data is the second aim. Since there is no such system available in the market, the project will have strong impacts in E-Commerce applications and bridge instrumentation systems useful to a wider community also advancing the research in clustering and expert systems.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL170100117
Funder
Australian Research Council
Funding Amount
$3,208,192.00
Summary
On snapping up semantics of dynamic pixels from moving cameras. The project aims to develop a suite of original models and algorithms for processing and understanding videos captured by moving cameras, and to establish the mathematical foundations for deep learning-based computer vision to provide theoretical underpinnings. The project expects to generate new knowledge that will transform moving-camera computer vision with step-changes in visual quality enhancement, compression and acceleration ....On snapping up semantics of dynamic pixels from moving cameras. The project aims to develop a suite of original models and algorithms for processing and understanding videos captured by moving cameras, and to establish the mathematical foundations for deep learning-based computer vision to provide theoretical underpinnings. The project expects to generate new knowledge that will transform moving-camera computer vision with step-changes in visual quality enhancement, compression and acceleration technologies, and solutions for fundamental computer vision tasks. A new concept of feature complexity for measuring the discriminant and learnable abilities of features from deep models will also be defined. The outcomes of the project will be critical for enabling autonomous machines to perceive and interact with the environment.Read moreRead less
Development of fundamental perception technology and algorithms for mining safety. The project will push the boundaries of mining safety research to deliver innovative and powerful tools to understand and control the level of risk of an operation. This knowledge will be used to develop algorithms to best assess safety issues in different scenarios, to design safety procedures and to develop operator training.
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